ODBMS Industry Watch » open source http://www.odbms.org/blog Trends and Information on Big Data, New Data Management Technologies, Data Science and Innovation. Sun, 02 Apr 2017 17:59:10 +0000 en-US hourly 1 http://wordpress.org/?v=4.2.13 On the new developments in Apache Spark and Hadoop. Interview with Amr Awadallah http://www.odbms.org/blog/2017/03/on-the-new-developments-in-apache-spark-and-hadoop-interview-with-amr-awadallah/ http://www.odbms.org/blog/2017/03/on-the-new-developments-in-apache-spark-and-hadoop-interview-with-amr-awadallah/#comments Mon, 13 Mar 2017 10:54:21 +0000 http://www.odbms.org/blog/?p=4326

“What this Big Data movement is about is using data to actually change our businesses in real-time (versus show the business leaders a report that they make a decision based on).”–Amr Awadallah

I have interviewed Amr Awadallah, Chief Technology Officer at Cloudera.  
Main topics of the interview are: the new developments in Apache Spark 2.0 Beta, and Hadoop  3.0.0-alpha1 release ; the lessons learned from Amr´s experience of using Hadoop at Yahoo!; and the business problems that world’s leading organisations do have.


Q1. Before Cloudera, you served as Vice President of Product Intelligence Engineering at Yahoo!, and ran one of the very first organisations to use Hadoop for data analysis and business intelligence. What are the main lessons you learned in that period?

Amr Awadallah: Couple of things. First, I learned that Hadoop is capable of solving all the business intelligence problems that I had at Yahoo.
(1) our systems weren’t scaling fast enough (we needed to cut down transformation times from hours to minutes),
(2) our systems weren’t economical on a $/TB basis thus making it hard to retain valuable data for longer time periods, and (3) we needed new methods to be able to store and analyze semi-structured (e.g. logs) and unstructured data (e.g. social media).
By implementing Hadoop in our team we saw first hand how it can address all these problems. The second lesson that I learned was that Hadoop, back then, was very rough to deploy and program against (it took us many months to deploy it and reprogram our transformations to run on it). It was these lessons that made it clear that there is room for a startup to focus on Hadoop since (1) it was solving a very real data problems that many organizations will face, and (2) it needed a lot of polish to make it work smoothly, securely, and reliably within the enterprise.

Q2. In 2008 you founded Cloudera together with Mike Olson (Oracle), Jeff Hammerbacher (Facebook) and Christophe Bisciglia (Google). What was your main motivation at that time?

Amr Awadallah: Pretty much to do what I describe above, we wanted to make the Hadoop technology easy to use for organizations. That included: (1) creating a distribution for Hadoop that bundles all the necessary open-source projects that make it work (we call that CDH, short for Cloudera Distribution for Apache Hadoop). (2) We also created a number of proprietary system management, security, and meta-data management tools around CDH to make it easier for organizations to deploy and operate Hadoop in production.

Q3. What are the typical challenging business problems that world’s leading organisations have?

Amr Awadallah: The technology we provide is very powerful and can be used to solve many problems across many industries, but we see four common themes: The first is simply using Hadoop as a faster, bigger, cheaper system for business intelligence and data analytics. i.e. a lot of organizations just use us to do things they have been doing already, just doing these things in a more economically scalable way.
The second use case is around deeper understanding of customers, i.e. moving away from segmenting all customers into a number of predefined buckets, but rather creating a dynamic micro-segment addressing each customer in a more precise way (thus reducing false positives).
The third use case is about using data to build better products and services, and this use-case is catalyzed by of the internet-of-things. Due to smart-sensors we are able to measure the real-world better than ever before; so this use-case is about taking all that data and leveraging it to either enhance our current product/service offerings, or build entirely new ones.
The fourth use case is about reducing business risk, and it manifests itself in a number of different sub-cases depending on the industry. For example, cyber-security is one of the key ways to reduce risk, and we have an open source project co-developed with Intel, called Apache Spot, which organizations can use to collect all their network flow data then use Spark machine learning algorithms to detect the anomalies in that data. Anti-money laundering and fraud detection is another way that our banking customers employ our platform to reduce risk within their businesses. Similarly, our insurance industry customers use our system to detect fraudulent claims, etc.

Q4. Can they be solved by analysing data? Can you give us some examples of how the use of advanced analytics drive business decisions?

Amr Awadallah: Yes, all the problems mentioned above can be solved with data. I want to highlight though that this isn’t necessarily about business decisions, which is what the Business Intelligence movement was about (we just help make that cheaper and faster). What this Big Data movement is about is using data to actually change our businesses in real-time (versus show the business leaders a report that they make a decision based on).
One of my favorite examples is a solution that one of our customers built to give voice to premature babies in neonatal intensive care units. They analyze the signals coming from the baby (sounds, blood pressure, heart rate, temperature, few brain signals), and based on that a message appears on the monitor above the infant showing the nurse if they are hungry, distressed from too much noise or light, etc.
That is really what we mean by using data to create new products and services that weren’t possible before (and not just reports/dashboard).

Q4. Graphs are important. Is it possible to do scalable graph analytics? If yes, how?

Amr Awadallah: Graphs are indeed important, a lot of our customer use-cases trace back to that (not just for social media analytics, but for example anti-money laundering requires analyzing relationships between many financial accounts for detecting bad behaviors, similarly for cyber security applications). I think scalability depends a fair bit on what’s being analyzed and how scalable we mean by scalable. But for most practical purposes I would say Spark’s GraphX is good enough. For example, you can compute PageRank fairly efficiently and scalably on a cluster using GraphX.

Q5. Data security is increasing important. The risk is due to the growing number of device endpoints. What solutions do exist to minimise such risk?

Amr Awadallah: A comprehensive enterprise data security strategy seeks to mitigate the risks presented by a growing number of potentially compromised endpoints connecting to corporate networks. Endpoint security will enable one or all of the following preventative controls:
The first is policy based enforcement of endpoint security configuration prior to granting and endpoint access to network based corporate assets. This ensures that any endpoint connected to corporate networks meets minimum requirements for endpoint security configuration.
The second measure is endpoint based anti-malware software (the existence of which may be a policy requirement to connect to the network per the first measure). Anti-malware prevents malicious code from infecting endpoints by monitoring for changes to system configuration and unusual activity or processes.
The third measure is endpoint encryption of corporate data on hard drives, folders and even removable media.
As mentioned above we also collaborate with Intel on Apache Spot, which tracks network flow patterns to detect anomalous communication behavior between different devices (including end point devices). Apache Spot just recently won InfoWorld 2017 Tech of the Year Award. Other advanced analytics security partners we closely work with are: CounterTack, Securonix, Niara, and Jask.

Q6. You recently announced the availability of an Apache Spark 2.0 Beta release for users of the Cloudera platform. How does it work? And how does it differ from the Hadoop-based data platform?

Amr Awadallah: First, at a meta-level, Hadoop (MapReduce specifically) was very good at achieving scalable computation by spreading jobs across many CPU cores and hard disk spindles. That said, MapReduce wasn’t very efficient in how it leveraged memory to optimize the performance of data processing pipelines that have many stages or iterations.
The main power of Spark, that made it take over from MapReduce, was how it truly leveraged memory to achieve better performance in deep or iterative data pipelines. That coupled with a simpler developer API made Spark take over very quickly from MapReduce.
Most of our new customer implementations for data processing or data science tend to be in Spark these days, versus MapReduce.
I should clarify however that this doesn’t mean that Hadoop is dead as some say. Apache Hadoop is comprised of three key subsystems: (1) MapReduce for computation, (2) YARN for resource scheduling, and (3) HDFS for storage. Spark only replaces MapReduce, we still rely heavily on both YARN and HDFS.

That said, the most notable features in Apache Spark 2.0 are:

1) Dataset API: It is a new API that represents the distributed collections of objects processed by Spark’s execution engine. It is an extension of Spark’s Dataframe API. It improves upon the Dataframe API by providing type-safe, object oriented programming interfaces. Users can now write User-Defined Functions and Lambda functions that provide compile time type safety. With the Dataset API, users benefit from optimized operations (like sort, join, hash, etc) in the SparkSQL engine, while also getting compile time type safety for user defined functions.

2) Model & Pipeline Persistence in Spark’s ML library: Machine learning Pipelines built with Spark’s ML library can now be serialized to a file and read back in.
The ability to save and reload these pipelines makes it easy for users to perform version control on the pipelines and safely distribute the pipelines. This helps in operationalizing them in production systems.

3) Structured Streaming: New stream processing API and engine that provides SQL like abstractions for authoring operations on data streams, and also improves performance by using the SparkSQL engine for processing the data streams. However, this is still an experimental API and not ready for production usage yet.

Besides the above 3 notable enhancements, there are a bunch of performance and scalability improvements across the board.

Q7. Apache Impala vs. Amazon Redshift: How Does Redshift Compare to Impala?

Amr Awadallah: Apache Impala is an analytic database engine architecturally designed to perform high-performance highly-concurrent SQL analytics on scalable, open data platforms like Hadoop’s HDFS and Amazon S3.
Impala decouples data storage from compute and lets users query data without having to move/load data specifically into an Impala storage-engine (it doesn’t have one). This architectural difference uniquely enables Impala to deliver a more flexible Business Intelligence experience than traditional database architectures like Redshift (which requires pre-loading the data).

Some of the key benefits of the Impala approach include:

* On-demand resources that are immediately ready to query existing S3 data without loading to a different data silo
* Ability to elastically grow/shrink clusters as needed due to decoupled storage and compute
* More predictable, multi-tenant isolation due to the ability to have multiple Impala clusters sharing a common S3 data repository
* Ability to share common data not only amongst Impala clusters, but also any application that runs on cloud-native S3 storage (for example, you can have both Apache Impala and Apache Spark run against the same data asset in S3, while it isn’t possible to have Apache Spark easily access the data stored in Redshift, it has to go through SQL first).
* Greater flexibility to explore new use cases, analytics, and data by directly querying S3 without rigid traditional data models and ETL

Not only does Impala deliver this additional flexibility, it does so at greater cost-performance and scalability compared to Redshift. See the following benchmark for data on that.

That said, Redshift’s sweet spot is in a different target as a smaller datamart as most Redshift installations are in the dozen of nodes range where Redshift’s limitations in scalability, elasticity, flexibility, and requirement to maintain separate copies of data are less critical.

Q8. What is Apache Kudu, and why is it relevant for Impala Users?

Amr Awadallah: Historically we had two storage engines in our distribution: (1) HDFS which is optimized for high-throughput analytics, but doesn’t support updates/inserts and (2) HBase which is optimized for low-latency updates/inserts but isn’t good for doing high-throughput queries. To build a proper data warehouse or time-series analytics system, you typically still need to make updates/inserts and that was why we created Apache Kudu.

Kudu is a new storage system that combines the benefits of both HDFS and HBase into one: it allows for low-latency updates/inserts, but also supports high-throughput analytical queries (i.e. fast analytics on fast moving data).
Unlike HDFS, Kudu is not a file-system, it is a record-based system, so the unit of storage is a record as opposed to a file. This allows Kudu to unlock Impala for real-time streaming applications that were not possible with HDFS.
In HDFS the data would only be visible to Impala after we finish closing the file, which typically happens after a large number of records are accumulated (that adds latency between when records are written to when they become visible to the analytical engine). With Kudu as soon as a record is written it is immediately visible to the Impala analytical engine. Finally, just like HDFS and HBase, the Kudu storage engine is fully integrated with our entire stack, not just Impala.
For example, you can also use Apache Spark for machine-learning jobs directly against Kudu.

Q9. The Apache Hadoop project recently announced its 3.0.0-alpha1 release. What is it?

Amr Awadallah: HDFS Erasure Encoding is really the main exciting new feature in Hadoop 3. Traditionally HDFS required three replicas, by default, for every data block to achieve durability, concurrent performance, and availability. Using erasure encoding techniques, HDFS in Hadoop 3 allows us to significantly reduce the storage overhead from 3x (i.e. 200%) to just 20% extra bits for parity. This will allow us to achieve the same durability benefits of 3x replication, but comes at the cost of potentially lower concurrent performance (when more than one job are trying to access the same block at same time) and lower availability resilience in face of top-of-rack switch failures (less of an issue these days).

Other cool additions are ATS v2 and classpath isolation which you can read more about here

Q10. What is the roadmap ahead for Cloudera Enterprise?

Amr Awadallah: We don’t discuss details of our product roadmap publicly, but there are three guiding themes for us in 2017: The first theme is fast-analytics on fast-moving data (which I covered above in regards to Kudu).
The second theme is cloud, which is making Cloudera Enterprise work better in cloud environments, and make it easier to move workloads (and skill sets) from on-premise clusters to transient cloud clusters in AWS, Azure, and/or Google Cloud.
The third theme is simplifying data-science and machine learning development, especially reducing the time from when a new algorithm is developed to how it can be deployed into production (stay tuned for more on that front).
Amr Awadallah, Ph.D. Chief Technology Officer, Cloudera
Before co-founding Cloudera in 2008, Amr (@awadallah) was an Entrepreneur-in-Residence at Accel Partners. Prior to joining Accel he served as Vice President of Product Intelligence Engineering at Yahoo!, and ran one of the very first organizations to use Hadoop for data analysis and business intelligence. Amr joined Yahoo after they acquired his first startup, VivaSmart, in July of 2000. Amr holds a Bachelor’s and Master’s degrees in Electrical Engineering from Cairo University, Egypt, and a Doctorate in Electrical Engineering from Stanford University.


Download Page for Apache Spark™

Apache Impala supported by Cloudera Enterprise

DATA-X: Videobook- 8 short videos introduce query analytics for Apache Hadoop

A package that allows R developers to use Hadoop HBase

Book: Big Data Analytics with Spark

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On in-memory, key-value data stores. Ofer Bengal and Yiftach Shoolman http://www.odbms.org/blog/2017/02/on-in-memory-key-value-data-stores-ofer-bengal-and-yiftach-shoolman/ http://www.odbms.org/blog/2017/02/on-in-memory-key-value-data-stores-ofer-bengal-and-yiftach-shoolman/#comments Mon, 13 Feb 2017 10:52:57 +0000 http://www.odbms.org/blog/?p=4278

“While modernizing legacy applications used to be a key reason for deploying in-memory, key-value data stores, we see that this is changing. New applications particularly those that are highly interactive need to bring a user experience that is very responsive under all conditions. For such new applications, an in-memory datastore, particularly one that can simplify run time analytics like counting, scoring, managing lists and sets, is becoming a key ingredient for low latency responses and high throughput.”  –Ofer Bengal.

I have interviewed Ofer Bengal, Co-Founder and CEO of Redis Labs, and Yiftach Shoolman, Co-Founder and CTO of Redis Labs.
Main topics of the interview are: How is the database market evolving, proprietary vs. open source software, in-memory/ key-value data stores, and the new features of Redis.


Q1. How do you see the database market evolving?

Ofer Bengal, Yiftach Shoolman: The main trends we identify today and believe will continue in upcoming years are:
1) Non-relational databases will continue to see growing adoption, because the schema framework is ineffective when it comes to unstructured data, change in data patterns, growing data volumes, more stringent performance requirements and the way modern apps are built.
2) Multiple database models as opposed to the absolute dominance of RDMS in the past few decades, each model solving the requirements of certain use cases.
Moreover, certain modern databases can run several database models (document, graph, etc.)
3) Multiple databases (different types or the same type) serving the same app. Modern applications are based on micro service architecture, in which each micro service works with the best database for its use case.
This creates new challenges for modern databases: (a) Instant provisioning – sometime hundreds or thousands of databases are provisioned within a second, and (b) Multi-tenancy, otherwise the cost associated with managing database infrastructure becomes extremely high.
4) Database-as-a-service is growing vs. self deployed and operated databases. With enterprises gradually moving to the cloud and having to deal with multiple type databases, it makes a lot of sense to outsource deployment and ongoing operations rather than building in-house practice of DBAs and Devops.
5) Hybrid transactional and analytical processing (HTAP). Driven by the need for application analytics to drive business decision making in real time, certain modern databases can handle those two different workloads simultaneously, eliminating the need for exporting transactional data to a separate dedicated analytical database.

Q2. Proprietary vs. open source software: what are the pros and cons?

Ofer Bengal, Yiftach Shoolman: From the community perspective, open source is great. If there is a vibrant community, it pushes innovation, problem solving and compatibility issues with different environments.
From users perspective, open source is “open”, accessible, can be used by anyone, transparent, and free of charge.
It often comes with less of a danger of vendor lock-in. It is very suitable for independent developers and startups. However enterprises using open source products may have certain challenges:
1. The product is not always suitable for enterprise workloads, especially when it comes to databases. Capabilities like infinite seamless scaling, high-availability with instant failover and stable performance at scale are not always the open source developer’s top priority.
2. Commercial support must be obtained and this typically comes with a price tag which is not much different than acquiring a commercial database product.
3. Commercial support is typically provided by a single company (most probably founded by the open source creators), which creates “vendor lock-in” by itself.
4. In the case of databases, using database-as-a-service may turn out to be lower in cost compared to provisioning cloud instances and running zero cost open source software on them, because commercial can be based on efficient multi-tenant architecture.

Q3. What is the current market for in-memory, key-value data stores?

Ofer Bengal: In-memory key-value data stores (sometimes called in-memory data grids (IMDGs)) have been around since more than a decade and have proven capable of supporting digital business needs for responsive, always-on user experience; real-time, actionable insights; and dynamic scaling. They are widely employed when you want to scale/modernize legacy applications without spending additional money on extremely expensive RDBMS licenses and hardware.This is achieved by providing a scalable and reliable in-memory datastore that enables low-latency transactional and analytical processing.
While modernizing legacy applications used to be a key reason for deploying in-memory, key-value data stores, we see that this is changing. New applications particularly those that are highly interactive need to bring a user experience that is very responsive under all conditions. For such new applications, an in-memory datastore, particularly one that can simplify run time analytics like counting, scoring, managing lists and sets, is becoming a key ingredient for low latency responses and high throughput.

From a Redis perspective, our innovation in data structures brings about the ability to simplify development to the extent that now most Redis users use it as a first responder and primary datastore for substantial pieces of their data. Furthermore with Redis’ data-structures, users can run operational and analytical use cases on the same database.
In addition, acceleration of other in-memory platforms like Spark is possible with Redis.

Gartner estimates that, in 2015, the stand-alone IMDG market was worth approximately $600 million, having grown by about 30% from the previous year. Gartner expects the market to continue to grow in the double-digit range through 2020 and to exceed $1 billion by 2018. Redis, one of the leaders in this space, grew in just a few years to be one of the most popular databases used by developers and enterprises.

Q4. Amazon ElastiCache supports two open-source in-memory engines: Redis and Memcached. What does it mean in practice?

Yiftach Shoolman: In practice, Amazon ElastiCache is a simple caching service that simplifies a developer experience by providing these two open source in-memory engines. Legacy applications that use simple cache can use ElastiCache seamlessly.
However, ElastiCache is single-tenant, limited to caching use cases and cannot be used as a database, lacking enterprise-grade functionalities such as infinite seamless scalability, instant failover and predictable performance.
The Redis Labs equivalent service, called Redis Cloud provides all the benefits of an enterprise-class Redis.

Q5. What are the pros and cons of Memcached and Redis?

Yiftach Shoolman: Redis can be thought of as modern database while memcached is older technology designed specifically for ephemeral caching.
The most important difference is in persistence and HA – memcached is not persistent nor HA, while Redis can operate as a full-fledged in-memory database, highly available through both in-memory replication and data persistence. This reflects the fact that caches in older architectures were not required to be highly available, but in modern architectures, built for scale and volume, cache outages can significantly impact the business and user experience.
Redis, the newer and more versatile technology allows individual data elements to be manipulated while memcached often incurs serialization/deserialization overheads that makes the entire application processing much slower. This is because Memcached can handle only simple key value use cases, whereas Redis offers many more data structures (hashes, sets, sorted sets, lists, hyperloglog..) that simplify complex data processing, analysis and operational use cases with ease.
Even when used as a cache, Redis has more sophisticated eviction policies which can be both active or passive while memcached has only a simple LRU and lazy eviction.
Redis and Memcached are both very popular open source projects, but given its richer functionality, more advanced design, many potential uses, and greater cost efficiency at scale, Redis should be your first choice in nearly every case.

Q6. For very large data sets or analytics workloads, running everything in-memory might not be cost effective. What is your take on this?

Ofer Bengal, Yiftach Shoolman: For very large data sets or analytics workloads, it is advantageous to utilize alternative memory technologies(such as Flash memory, which is a tenth of the cost), as extensions of memory rather than impose a disk access penalty. We have extended enterprise Redis in this manner to take advantage of Flash memory, while using a tiered approach (keys and hot values are still in the fastest memory, while cold values are in “slower” Flash memory) to ensure that you still see sub-millisecond latencies with millions of ops/sec throughput.

Q7. Redis was created by Salvatore Sanfilippo in 2009. What is his role today?

Ofer Bengal: Salvatore is leading the development of open source Redis within Redis Labs. He works with a group of experienced developers on extending the capabilities of Redis. A good example of this collaborative works is the recent introduction of Redis Modules, which extend Redis to a variety of new modern use cases. Salvatore wrote the API and the other team members in a very short time created and tested a few modules, such as Redisearch (a full-text search engine) and Redis-ML (enhancing the performance of Spark machine learning capabilities). Salvatore’s role is to continue the community innovation around the Redis core, together with his team of Redis Labs developers.

Q8. What are the differences of Redis Labs` version of Redis with the original one developed in 2009?

Yiftach Shoolman: Redis Labs fully supports the open source Redis versions, but enhances them with a container-like layer that adds a proxy, cluster management and a shared nothing architecture. Taken together, Redis Labs provides a solid enterprise foundation to Redis, allowing it to scale seamlessly in memory across many hundreds of servers with the high availability through persistence, in-memory cross-rack/zone/region/datacenter replication and instant automatic failover. No retooling or re-architecting is required to move from open source Redis to enterprise Redis, the process is basically effortless and immediate. Redis Labs also offers various database modules, like a RediSearch, multiple probabilistic modules like Bloom Filter, TopK, CMS, Redis-ML for Machine Learning, Redis-TS for Time Series processing, JSON and Graph support.

Q9. What are the possible scenarios of using Redis for data analytics?

Ofer Bengal, Yiftach Shoolman: Redis data structures come with built-in simple analytic operations like counting, ranking, scoring, ranges and more. Over time, probabilistic data structures have added the ability to analytically estimate millions and trillions of events, without requiring memory to store all of the events.
Set operations have made it possible to simplify comparisons, intersections, unions of sets – analytics that are usually complicated with data stores. RQL (Redis SQL) and secondary indexing, allows executing complex SQL queries on an existing Redis database. And finally recent modules like RediSearch, Neural Redis and Redis-ML have added advanced search and machine learning capabilities – not naturally occurring in any other databases.
With all of these possibilities, and with the move to automated decision making, we see increasing usage of Redis for data analytics scenarios.

Q10. How safe is a Redis server?

Yiftach Shoolman: The Redis enterprise server comes with client-based SSL authentication, built-in cloud firewall support (when running on public clouds), password authentication and role-based authorization that enables customizing security levels.

Qx. Anything else you wish to add?

Ofer Bengal: Redis is a game -changer when it comes to databases, and its progression over the last seven years has demonstrated that the industry and market are demanding performance and increasing flexibility to deal with all types of data processing, storage and analytic scenarios. Redis’ core values have always included high performance, high throughput and very low latencies. With the visionary addition of modules. The community has turned it into an all purpose datastore – suitable for any scenario that needs a database.


Ofer BengalCo-Founder and CEO of Redis Labs
Ofer is a serial entrepreneur who has founded and led several companies in the areas of data communications, telecommunications, Internet, homeland security and medical devices. Ofer was founder & CEO of RIT Technologies (NASDAQ: RITT), a provider of sophisticated telecommunications and data communications systems to major world carriers. He began his career as an aerospace engineer in the Israeli Air Force and then built his own aerospace engineering consulting firm. As a hobby, he has also invented, developed and licensed toy concepts to companies such as Milton Bradley, Hasbro and Tomy. Ofer holds a Bachelor of Science (cum laude) in aerospace engineering from the Technion, Israel Institute of Technology.

Yiftach ShoolmanCo-Founder and CTO of Redis Labs
Yiftach is an experienced technologist, having held leadership engineering and product roles in diverse fields from application acceleration, cloud computing and software-as-a-service (SaaS), to broadband networks and metro networks. He was the founder, president and CTO of Crescendo Networks (acquired by F5, NASDAQ:FFIV), the vice president of software development at Native Networks (acquired by Alcatel, NASDAQ: ALU) and part of the founding team at ECI Telecom broadband division, where he served as vice president of software engineering. Yiftach holds a Bachelor of Science in Mathematics and Computer Science and has completed studies for Master of Science in Computer Science at Tel-Aviv University.

Redis Cloud Now Available with Integrated Billing through AWS Marketplace- News Release- January 10, 2017.

AWS SaaS Marketplace.

Redis Documentation

EBOOK – REDIS IN ACTION This book covers the use of Redis, an in-memory database/data structure server.

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Big Data and The Great A.I. Awakening. Interview with Steve Lohr http://www.odbms.org/blog/2016/12/big-data-and-the-great-a-i-awakening-interview-with-steve-lohr/ http://www.odbms.org/blog/2016/12/big-data-and-the-great-a-i-awakening-interview-with-steve-lohr/#comments Mon, 19 Dec 2016 08:35:56 +0000 http://www.odbms.org/blog/?p=4274

“I think we’re just beginning to grapple with implications of data as an economic asset” –Steve Lohr.

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening.

Wishing you all the best for the Holiday Season and a healthy and prosperous New Year!


Q1. Why do you think Google (TensorFlow) and Microsoft (Computational Network Toolkit) are open-sourcing their AI software?

Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses. But I also think the move is a recognition that algorithms are not where their long-term advantage lies. Data is.

Q2. What are the implications of that for both business and policy?

Steve Lohr: The companies with big data pools can have great economic power. Today, that shortlist would include Google, Microsoft, Facebook, Amazon, Apple and Baidu.
I think we’re just beginning to grapple with implications of data as an economic asset. For example, you’re seeing that now with Microsoft’s plan to buy LinkedIn, with its personal profiles and professional connections for more than 400 million people. In the evolving data economy, is that an antitrust issue of concern?

Q3. In this competing world of AI, what is more important, vast data pools, sophisticated algorithms or deep pockets?

Steve Lohr: The best answer to that question, I think, came from a recent conversation with Andrew Ng, a Stanford professor who worked at GoogleX, is co-founder of Coursera and is now chief scientist at Baidu. I asked him why Baidu, and he replied there were only a few places to go to be a leader in A.I. Superior software algorithms, he explained, may give you an advantage for months, but probably no more. Instead, Ng said, you look for companies with two things — lots of capital and lots of data. “No one can replicate your data,” he said. “It’s the defensible barrier, not algorithms.”

Q4. What is the interplay and implications of big data and artificial intelligence?

Steve Lohr: The data revolution has made the recent AI advances possible. We’ve seen big improvements in the last few years, for example, in AI tasks like speech recognition and image recognition, using neural network and deep learning techniques. Those technologies have been around for decades, but they are getting a huge boost from the abundance of training data because of all the web image and voice data that can be tapped now.

Q5. Is data science really only a here-and-now version of AI?

Steve Lohr: No, certainly not only. But I do find that phrase a useful way to explain to most of my readers — intelligent people, but not computer scientists — the interplay between data science and AI. To convey that rudiments of data-driven AI are already all around us. It’s not — surely not yet — robot armies and self-driving cars as fixtures of everyday life. But it is internet search, product recommendations, targeted advertising and elements of personalized medicine, to cite a few examples.

Q6. Technology is moving beyond increasing the odds of making a sale, to being used in higher-stakes decisions like medical diagnosis, loan approvals, hiring and crime prevention. What are the societal implications of this?

Steve Lohr: The new, higher-stakes decisions that data science and AI tools are increasingly being used to make — or assist in making — are fundamentally different than marketing and advertising. In marketing and advertising, a decision that is better on average is plenty good enough. You’ve increased sales and made more money. You don’t really have to know why.
But the other decisions you mentioned are practically and ethically very different. These are crucial decisions about individual people’s lives. Better on average isn’t good enough. For these kinds of decisions, issues of accuracy, fairness and discrimination come into play.
That, I think, argues for two things. First, some sort of auditing tool; the technology has to be able to explain itself, to explain how a data-driven algorithm came to the decision or recommendation that it did.
Second, I think it argues for having a “human in the loop” for most of these kinds of decisions for the foreseeable future.

Q7. Will data analytics move into the mainstream of the economy (far beyond the well known, born-on-the-internet success stories like Google, Facebook and Amazon)?

Steve Lohr: Yes, and I think we’re seeing that now in nearly every field — health care, agriculture, transportation, energy and others. That said, it is still very early. It is a phenomenon that will play out for years, and decades.
Recently, I talked to Jeffrey Immelt, the chief executive of General Electric, America’s largest industrial company. GE is investing heavily to put data-generating sensors on its jet engines, power turbines, medical equipment and other machines — and to hire software engineers and data scientists.
Immelt said if you go back more than a century to the origins of the company, dating back to Thomas Edison‘s days, GE’s technical foundation has been materials science and physics. Data analytics, he said, will be the third fundamental technology for GE in the future.
I think that’s a pretty telling sign of where things are headed.

Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years and writes for the Times’ Bits blog. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting.
He was a foreign correspondent for a decade and served as an editor, and has written for national publications such as the New York Times Magazine, the Atlantic, and the Washington Monthly. He is the author of Go To: The Story of the Math Majors, Bridge Players, Engineers, Chess Wizards, Maverick Scientists, Iconoclasts—the Programmers Who Created the Software Revolution and Data-ism The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else.
He lives in New York City.



Google (TensorFlow): TensorFlow™ is an open source software library for numerical computation using data flow graphs.

Microsoft (Computational Network Toolkit): A free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain.

Data-ism The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. by Steve Lohr. 2016 HarperCollins Publishers

Related Posts

Don’t Fear the Robots. By STEVE LOHR. -OCT. 24, 2015-The New York Times, SundayReview | NEWS ANALYSIS

G.E., the 124-Year-Old Software Start-Up. By STEVE LOHR. -AUG. 27, 2016- The New York Times, TECHNOLOGY

Machines of Loving Grace. Interview with John Markoff. ODBMS Industry Watch, Published on 2016-08-11

Recruit Institute of Technology. Interview with Alon Halevy. ODBMS Industry Watch, Published on 2016-04-02

Civility in the Age of Artificial Intelligence, by STEVE LOHR, technology reporter for The New York Times, ODBMS.org

On Artificial Intelligence and Society. Interview with Oren Etzioni, ODBMS Industry Watch.

On Big Data and Society. Interview with Viktor Mayer-Schönberger, ODBMS Industry Watch.

Follow us on Twitter:@odbmsorg


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How the 11.5 million Panama Papers were analysed. Interview with Mar Cabra http://www.odbms.org/blog/2016/10/how-the-11-5-million-panama-papers-were-analysed-interview-with-mar-cabra/ http://www.odbms.org/blog/2016/10/how-the-11-5-million-panama-papers-were-analysed-interview-with-mar-cabra/#comments Tue, 11 Oct 2016 17:54:36 +0000 http://www.odbms.org/blog/?p=4214

“The best way to explore all The Panama Papers data was using graph database technology, because it’s all relationships, people connected to each other or people connected to companies.” –Mar Cabra.

I have interviewed Mar Cabra, head of the Data & Research Unit of the International Consortium of Investigative Journalists (ICIJ). Main subject of the interview is how the 11.5 million Panama Papers were analysed.


Q1. What is the mission of the International Consortium of Investigative Journalists (ICIJ)?

Mar Cabra: Founded in 1997, the ICIJ is a global network of more than 190 independent journalists in more than 65 countries who collaborate on breaking big investigative stories of global social interest.

Q2. What is your role at ICIJ?

Mar Cabra: I am the Editor at the Data and Research Unit – the desk at the ICIJ that deals with data, analysis and processing, as well as supporting the technology we use for our projects.

Q3. The Panama Papers investigation was based on a 2.6 Terabyte trove of data obtained by Süddeutsche Zeitung and shared with ICIJ and a network of more than 100 media organisations. What was your role in this data investigation?

Mar Cabra: I co-ordinated the work of the team of developers and journalists that first got the leak from Süddeutsche Zeitung, then processed it to make it available online though secure platforms with more than 370 journalists.
I also supervised the data analysis that my team did to enhance and focus the stories. My team was also in charge of the interactive product that we produced for the publication stage of The Panama Papers, so we built an interactive visual application called the ‘Powerplayers’ where we detailed the main stories of the politicians with connections to the offshore world. We also released a game explaining how the offshore world works! Finally, in early May, we updated the offshore database with information about the Panama Papers companies, the 200,000-plus companies connected with Mossack Fonseca.

Q4. The leaked dataset are 11.5 million files from Panamanian law firm Mossack Fonseca. How was all this data analyzed?

Mar Cabra: We relied on Open Source technology and processes that we had worked on in previous projects to process the data. We used Apache Tika to process the documents and also to access them, and created a processing chain of 30 to 40 machines in Amazon Web Services which would process in parallel those documents, then index them onto a document search platform that could be used by 100s of journalists from anywhere in the world.

Q5. Why did you decide to use a graph-based approach for that?

Mar Cabra: Inside the 11.5 million files in the original dataset given to us, there were more than 3 million that came from Mossaka Fonseca’s internal database, which basically contained names of companies in offshore jurisdictions and the people behind them. In other words, that’s a graph! The best way to explore all The Panama Papers data was using graph database technology, because it’s all relationships, people connected to each other or people connected to companies.

Q6. What were the main technical challenges you encountered in analysing such a large dataset?

Mar Cabra: We had already used all the tools that we were using in this investigation, in previous projects. The main issue here was dealing with many more files in many more formats. So the main challenge was how can we make readable all those files, which in many cases were images, in a fast way.
Our next problem was how could we make them understandable to journalists that are not tech savvy. Again, that’s where a graph database became very handy, because you don’t need to be a data scientist to work with a graph representation of a dataset, you just see dots on a screen, nodes, and then just click on them and find the connections – like that, very easily, and without having to hand-code or build queries. I should say you can build queries if you want using Cypher, but you don’t have to.

Q7. What are the similarities with the way you analysed data in the Swiss Leaks story (exposing the fraudulent activity of 100,000 HSBC private bank clients in Switzerland)?

Mar Cabra: We used the same tools for that – a document search platform and a graph database and we used them in combination to find stories. The baseline was the same but the complexity was 100 times more for the Panama Papers. So the technology is the same in principle, but because we were dealing with many more documents, much more complex data, in many more formats, we had to make a lot of improvements in the tools so they really worked for this project. For example, we had to improve the document search platform with a batch search feature, where journalists would upload a list of names and then they would get a list back of links when that list of names had a hit a document.

Q8. Emil Eifrem, CEO, Neo Technology wrote: “If the Panama Papers leak had happened ten years ago, no story would have been written because no one else would have had the technology and skillset to make sense of such a massive dataset at this scale.” What is your take on this?

Mar Cabra: We would have done the Panama Papers papers differently, probably printing the documents – and that would have had a tremendous effect on the paper supplies of the world, because printing out all 11.5 million files would have been crazy! We would have published some stories and the public might have seen some names on the front page of a few newspapers, but the scale and the depth and the understanding of this complex world would not have been able to happen without access to the technology we have today. We would just have not been able to do such an in-depth investigation at a global scale without the technology we have access to now.

Q9. Whistleblowers take incredible risks to help you tell data stories. Why do they do it?

Mar Cabra: Occasionally, some whistleblowers have a grudge and are motivated in more personal terms. Many have been what we call in Spanish ‘widows of power’: people who have been in power and have lost it, and those who wish to expose the competition or have a grudge. Motivations of Whistleblowers vary, but I think there is always an intention to expose injustice. ‘John Doe’ is the source behind the Panama Papers, and a few weeks after we published, he explained his motivation; he wanted to expose an unjust system.

Mar Cabra is the head of ICIJ’s Data & Research Unit, which produces the organization’s key data work and also develops tools for better collaborative investigative journalism. She has been an ICIJ staff member since 2011, and is also a member of the network.

Mar fell in love with data while being a Fulbright scholar and fellow at the Stabile Center for Investigative Journalism at Columbia University in 2009/2010. Since then, she’s promoted data journalism in her native Spain, co-creating the first ever masters degree on investigative reporting, data journalism and visualisation  and the national data journalism conference, which gathers more than 500 people every year.

She previously worked in television (BBC, CCN+ and laSexta Noticias) and her work has been featured in the International Herald Tribune, The Huffington Post, PBS, El País, El Mundo or El Confidencial, among others.
In 2012 she received the Spanish Larra Award to the country’s most promising journalist under 30. (PGP public key)


– Panama Papers Source Offers Documents To Governments, Hints At More To Come. International Consortium of Investigative Journalists. May 6, 2016

The Panama Papers. ICIJ

– The two journalists from Sueddeutsche ZeitungFrederik Obermaier and Bastian Obermayer

– Offshore Leaks Database: Released in June 2013, the Offshore Leaks Database is a simple search box.

Open Source used for analysing the #PanamaPapers:

– Oxwall: We found an open source social network tool called Oxwall that we tweaked to our advantage. We basically created a private social network for our reporters.

– Apache Tika and Tesseract to do optical character recognition (OCR),

– We created a small program ourselves which we called Extract which is actually in our GitHub account that allowed us to do this parallel processing. Extract would get a file and try to see if it could recognize the content. If it couldn’t recognize the content, then we would do OCR and then send it to our document searching platform, which was Apache Solr.

– Based on Apache Solr, we created an index, and then we used Project Blacklight, another open source tool that was originally used for libraries, as our front-end tool. For example, Columbia University Library, where I studied, used this tool.

– Linkurious: Linkurious is software that allows you to visualize graphs very easily. You get a license, you put it in your server, and if you have a database in Neo4j you just plug it in and within hours you have the system set up. It also has this private system where our reporters can login or logout.

– Thanks to another open source tool – in this case Talend – and extractions from a load tool, we were able to easily transform our database into Neo4j, plug in Linkurious and get reporters to search.

Neo4j: Neo4j is a highly scalable, native graph database purpose-built to leverage not only data but also its relationships. Neo4j’s native graph storage and processing engine deliver constant, real-time performance, helping enterprises build intelligent applications to meet today’s evolving data challenges.

-The good thing about Linkurious is that the reporters or the developers at the other end of the spectrum can also make highly technical Cypher queries if they want to start looking more in depth at the data.

Related Posts


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Database Challenges and Innovations. Interview with Jim Starkey http://www.odbms.org/blog/2016/08/database-challenges-and-innovations-interview-with-jim-starkey/ http://www.odbms.org/blog/2016/08/database-challenges-and-innovations-interview-with-jim-starkey/#comments Wed, 31 Aug 2016 03:33:42 +0000 http://www.odbms.org/blog/?p=4218

“Isn’t it ironic that in 2016 a non-skilled user can find a web page from Google’s untold petabytes of data in millisecond time, but a highly trained SQL expert can’t do the same thing in a relational database one billionth the size?.–Jim Starkey.

I have interviewed Jim Starkey. A database legendJim’s career as an entrepreneur, architect, and innovator spans more than three decades of database history.


Q1. In your opinion, what are the most significant advances in databases in the last few years?

Jim Starkey: I’d have to say the “atom programming model” where a database is layered on a substrate of peer-to-peer replicating distributed objects rather than disk files. The atom programming model enables scalability, redundancy, high availability, and distribution not available in traditional, disk-based database architectures.

Q2. What was your original motivation to invent the NuoDB Emergent Architecture?

Jim Starkey: It all grew out of a long Sunday morning shower. I knew that the performance limits of single-computer database systems were in sight, so distributing the load was the only possible solution, but existing distributed systems required that a new node copy a complete database or partition before it could do useful work. I started thinking of ways to attack this problem and came up with the idea of peer to peer replicating distributed objects that could be serialized for network delivery and persisted to disk. It was a pretty neat idea. I came out much later with the core architecture nearly complete and very wrinkled (we have an awesome domestic hot water system).

Q3. In your career as an entrepreneur and architect what was the most significant innovation you did?

Jim Starkey: Oh, clearly multi-generational concurrency control (MVCC). The problem I was trying to solve was allowing ad hoc access to a production database for a 4GL product I was working on at the time, but the ramifications go far beyond that. MVCC is the core technology that makes true distributed database systems possible. Transaction serialization is like Newtonian physics – all observers share a single universal reference frame. MVCC is like special relativity, where each observer views the universe from his or her reference frame. The views appear different but are, in fact, consistent.

Q4. Proprietary vs. open source software: what are the pros and cons?

Jim Starkey: It’s complicated. I’ve had feet in both camps for 15 years. But let’s draw a distinction between open source and open development. Open development – where anyone can contribute – is pretty good at delivering implementations of established technologies, but it’s very difficult to push the state of the art in that environment. Innovation, in my experience, requires focus, vision, and consistency that are hard to maintain in open development. If you have a controlled development environment, the question of open source versus propriety is tactics, not philosophy. Yes, there’s an argument that having the source available gives users guarantees they don’t get from proprietary software, but with something as complicated as a database, most users aren’t going to try to master the sources. But having source available lowers the perceived risk of new technologies, which is a big plus.

Q5. You led the Falcon project – a transactional storage engine for the MySQL server- through the acquisition of MySQL by Sun Microsystems. What impact did it have this project in the database space?

Jim Starkey: In all honesty, I’d have to say that Falcon’s most important contribution was its competition with InnoDB. In the end, that competition made InnoDB three times faster. Falcon, multi-version in memory using the disk for backfill, was interesting, but no matter how we cut it, it was limited by the performance of the machine it ran on. It was fast, but no single node database can be fast enough.

Q6. What are the most challenging issues in databases right now?

Jim Starkey: I think it’s time to step back and reexamine the assumptions that have accreted around database technology – data model, API, access language, data semantics, and implementation architectures. The “relational model”, for example, is based on what Codd called relations and we call tables, but otherwise have nothing to do with his mathematic model. That model, based on set theory, requires automatic duplicate elimination. To the best of my knowledge, nobody ever implemented Codd’s model, but we still have tables which bear a scary resemblance to decks of punch cards. Are they necessary? Or do they just get in the way?
Isn’t it ironic that in 2016 a non-skilled user can find a web page from Google’s untold petabytes of data in millisecond time, but a highly trained SQL expert can’t do the same thing in a relational database one billionth the size?. SQL has no provision for flexible text search, no provision for multi-column, multi-table search, and no mechanics in the APIs to handle the results if it could do them. And this is just one a dozen problems that SQL databases can’t handle. It was a really good technical fit for computers, memory, and disks of the 1980’s, but is it right answer now?

Q7. How do you see the database market evolving?

Jim Starkey: I’m afraid my crystal ball isn’t that good. Blobs, another of my creations, spread throughout the industry in two years. MVCC took 25 years to become ubiquitous. I have a good idea of where I think it should go, but little expectation of how or when it will.

Qx. Anything else you wish to add?

Jim Starkey: Let me say a few things about my current project, AmorphousDB, an implementation of the Amorphous Data Model (meaning, no data model at all). AmorphousDB is my modest effort to question everything database.
The best way to think about Amorphous is to envision a relational database and mentally erase the boxes around the tables so all records free float in the same space – including data and metadata. Then, if you’re uncomfortable, add back a “record type” attribute and associated syntactic sugar, so table-type semantics are available, but optional. Then abandon punch card data semantics and view all data as abstract and subject to search. Eliminate the fourteen different types of numbers and strings, leaving simply numbers and strings, but add useful types like URL’s, email addresses, and money. Index everything unless told not to. Finally, imagine an API that fits on a single sheet of paper (OK, 9 point font, both sides) and an implementation that can span hundreds of nodes. That’s AmorphousDB.

Jim Starkey invented the NuoDB Emergent Architecture, and developed the initial implementation of the product. He founded NuoDB [formerly NimbusDB] in 2008, and retired at the end of 2012, shortly before the NuoDB product launch.

Jim’s career as an entrepreneur, architect, and innovator spans more than three decades of database history from the Datacomputer project on the fledgling ARPAnet to his most recent startup, NuoDB, Inc. Through the period, he has been
responsible for many database innovations from the date data type to the BLOB to multi-version concurrency control (MVCC). Starkey has extensive experience in proprietary and open source software.

Starkey joined Digital Equipment Corporation in 1975, where he created the Datatrieve family of products, the DEC Standard Relational Interface architecture, and the first of the Rdb products, Rdb/ELN. Starkey was also software architect for DEC’s database machine group.

Leaving DEC in 1984, Starkey founded Interbase Software to develop relational database software for the engineering workstation market. Interbase was a technical leader in the database industry producing the first commercial implementations of heterogeneous networking, blobs, triggers, two phase commit, database events, etc. Ashton-Tate acquired Interbase Software in 1991, and was, in turn, acquired by Borland International a few months later. The Interbase database engine was released open source by Borland in 2000 and became the basis for the Firebird open source database project.

In 2000, Starkey founded Netfrastructure, Inc., to build a unified platform for distributable, high quality Web applications. The Netfrastructure platform included a relational database engine, an integrated search engine, an integrated Java virtual machine, and a high performance page generator.

MySQL, AB, acquired Netfrastructure, Inc. in 2006 to be the kernel of a wholly owned transactional storage engine for the MySQL server, later known as Falcon. Starkey led the Falcon project through the acquisition of MySQL by Sun Microsystems.

Jim has a degree in Mathematics from the University of Wisconsin.
For amusement, Jim codes on weekends, while sailing, but not while flying his plane.



NuoDB Emergent Architecture (.PDF)

On Database Resilience. Interview with Seth Proctor, ODBMs Industry Watch, March 17, 2015

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– Challenges and Opportunities of The Internet of Things. Interview with Steve Cellini, ODBMS Industry Watch, October 7, 2015

– Hands-On with NuoDB and Docker, BY MJ Michaels, NuoDB. ODBMS.org– OCT 27 2015

– How leading Operational DBMSs rank popularity wise? By Michael Waclawiczek– ODBMS.org · JANUARY 27, 2016

– A Glimpse into U-SQL BY Stephen Dillon, Schneider Electric, ODBMS.org-DECEMBER 7, 2015

– Gartner Magic Quadrant for Operational DBMS 2015

Follow us on Twitter: @odbmsorg


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Using NoSQL for Ireland’s Online Tax Research Database. http://www.odbms.org/blog/2016/05/using-nosql-for-irelands-online-tax-research-database/ http://www.odbms.org/blog/2016/05/using-nosql-for-irelands-online-tax-research-database/#comments Mon, 02 May 2016 08:18:17 +0000 http://www.odbms.org/blog/?p=4128

“When the Institute began to look for a new platform, it became apparent that a relational database was not the best solution to effectively manage and deliver our XML content.”–Martin Lambe.

The Irish Tax Institute is the leading representative and educational body for Ireland’s AITI Chartered Tax Advisers (CTA) and is the only professional body exclusively dedicated to tax. One of their service is TaxFind – Ireland’s Leading Online Tax Research Database, offering Search to 200,000 pages of tax content, over 8,000 pages of Irish tax legislation, Irish Tax Institute tax technical papers, over 25 leading tax commentary publications, and 1000s of Irish Tax Review articles.

I did a joint interview with Martin Lambe, CEO of the Irish Tax Institute and Sam Herbert, Client Services Director at 67 Bricks.
Main topics of the interview are the data challenges they currently face, and the implementation of TaxFind using MarkLogic.


Q1. What are the main data challenges you currently have at the Irish Tax Institute?

Martin Lambe: The Irish Tax Institute moved its publication workflow to an XML-based process in 2009 and we have a large archive of valuable tax information contained in quite complex XML format. The main challenge was to find a solution that could store the repository of data (XML and other formats) and provide a simple search interface that directs users very quickly to the most relevant result. The “findability” of relevant content is crucial.

Q2. What is the TaxFind research database?

Martin Lambe: The Irish Tax Institute is the main provider of tax information in Ireland and TaxFind is the Institute’s online tax research database. TaxFind offers subscribers access to Irish tax legislation and guidance that includes tax technical papers from seminars and conferences, as well as over 30 tax commentary publications. It is used by thousands of CTAs in Ireland on a daily basis to assist in their tax research.

Q3. Who are the members that benefit from this TaxFind research database?

Martin Lambe: TaxFind serves the Chartered Tax Adviser (CTA) community in Ireland and other tax professionals such as those in the global accounting firms.

Q4. Why did you discard your previous implementation with a relational database system?

Martin Lambe: The previous database was literally creaking at the seams. Users were increasingly frustrated with difficulties accessing the database on different browsers and the old platform did not support mobile devices or tablets. When the Institute began to look for a new platform, it became apparent that a relational database was not the best solution to effectively manage and deliver our XML content. XML content stored in a NoSQL document database is indexed specifically for the search engine and this means the performance of our search engine and the relevancy of results is dramatically improved.

Q5. Why did you select MarkLogic`s NoSQL database platform?

Sam Herbert: MarkLogic is scalable to support fast querying across large amounts of data, it deals with XML content very well (and most of the tax data is either in XML, or in HTML that can be treated as XHTML), and has good searching. It is also a good environment to develop in – it has excellent documentation, and good tooling. It helps that it uses XQuery as one of its query languages, rather than a proprietary database-specific language.

Q6. Is SQL still important for you?

Sam Herbert: I don’t think it’s true to say that any particular type of technology is “important” to ITI – it’s all about how it can benefit users. From a 67 Bricks perspective, we work with relational databases, NoSQL databases, and graph databases depending on what shape the data is and what the needs are around querying it.

Q7 Why not choose an open source solution?

Sam Herbert: We’re using Open Source components in other parts of the system, and we’re keen on using Open Source where possible. However, for the data store, there aren’t any Open Source alternatives that have the combination of good scalability, good support for XML content, a standard query language, and powerful searching that we were looking for.

Q8. Can you tell us a bit about the architecture of the new implementation of the TaxFind research database

Sam Herbert: There are three major components:

– a frontend display and service layer written using the Play framework
– the MarkLogic data store
– a semantic enrichment component using Semaphore SmartLogic and the ITI taxonomy

The Play component is what users interact with – both for human users coming to the web site, and automated use of the web services. The bulk of the data retrieval and manipulation is done via a set of XQuery functions defined within the MarkLogic store. When new data is uploaded, it is processed within the Play code, enriched using Semaphore SmartLogic, and then stored in MarkLogic.

Q9. How do you manage to integrate Irish Tax Institute`s tax data, bringing together in excess of 300,000 pages of tax content including archive material in Word, PDF, XML and HTML?

Sam Herbert: The most complex part of the data is the XML content. These are very large XML files representing legislation, books, and other tax materials, that are inter-related in complex ways, and with a lot of deeply nested hierarchy. An important part of managing the data was splitting these into appropriately sized fragments, and then identifying the linking between different files – for example a piece of legislation will refer to other legislation, and commentary will refer to that legislation, and a new piece of legislation may supersede an earlier piece.

The non-XML content is larger in volume, but each individual document is smaller and is structurally simpler. Managing this content was largely a matter of loading it in and letting it be indexed.

Q10. How do you capture and digitize information in various formats and make it searchable?

Sam Herbert: Making it searchable is straightforward – it’s making it searchable in ways that support the expectations of the users that’s much more difficult.

A good search experience requires both subject matter expertise and good automated tests.

The basic search is using MarkLogic’s full text search. The next step was to work with tax experts within and outside the ITI to identify appropriate facets within the content with which to group the results – based on a combination of what the user requirements were and what was supported by the data.

There were additional complexities around weighting the search results to make the “best” results come at the top in as many circumstances as possible – for example, weighting terms within headings, weighting more recent content, weighting content based on its category so legislation is more important than commentary, and weighting content higher based on its popularity. The semantic enrichment based on tax terms from the ITI taxonomy also enhances the searching.

Q11. How do you ensure that this solution is scalable?

Sam Herbert: The solution is deployed to a load-balanced cluster using Amazon Web Services. The Play frontend is purely stateless REST. This means that we can scale to support more users easily by spinning up more servers – and using AWS makes this easy. Overall, using AWS has been a big win for us, in terms of being able to get servers running easily, being able to increase and decrease things like their memory size easily, and the various ancillary services it provides like DNS and load balancing. By making sure we can scale to support additional data, we can use MarkLogic effectively.


Martin Lambe is Chief Executive of the Irish Tax Institute. His previous role within the Institute was that of Director of Finance.

Sam Herbert is Client Services Director at 67 Bricks, a company that works with information owners (particularly publishers) who want to enrich their content to make it more structured, granular, flexible and reusable.
67 Bricks utilises its deep understanding of the content enrichment challenge to help publishers develop systems and capabilities to increase the value of their content. With expertise in XML, business analysis, semantic tagging and software development, 67 Bricks works closely with its clients to develop and implement content enrichment capabilities and enriched content digital products.


Irish Tax Institute


67 Bricks


Related Posts

The rise of immutable data stores. By Alan Morrison, Senior Manager, PwC Center for technology and innovation (CTI). ODBMS.org

Unthink: Moving Beyond the Constraints of Relational Databases. by Tom McGrath, MarkLogic. ODBMS.org March 14, 2016.

MarkLogic Case Study: Royal Society of Chemistry.ODBMS.org

On making information accessible. Interview with David Leeming. ODBMS Industry Watch, on July 30, 2014

Follow us on Twitter: @odbmsorg


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On Big Data and Data Science. Interview with James Kobielus http://www.odbms.org/blog/2016/04/on-big-data-and-data-science-interview-with-james-kobielus/ http://www.odbms.org/blog/2016/04/on-big-data-and-data-science-interview-with-james-kobielus/#comments Tue, 19 Apr 2016 08:34:09 +0000 http://www.odbms.org/blog/?p=4119

“One of the most typical mistakes in large-scale data projects is losing sight of the biases that may skew the insights you extract.”– James Kobielus

On the topics of Big Data, and Data Science, I have interviewed James Kobielus, IBM Big Data Evangelist.


Q1. What kind of companies generate Big Data, besides the Internet giants?

James Kobielus: Big data isn’t something you “generate.” Rather, the term refers to the ability to achieve differentiated value from advanced analytics on trustworthy data at any scale. In other words, it’s a best practice, not a specific type of data or even a specific scale of data (measured in volume, velocity, and/or variety).

When considered in this light, you can identify big data analytic applications in every industry. Every C-level executive has strategic applications of big data. Here are just a smattering:

  • Chief Marketing Officers have been the prime movers on many big data initiatives that involve Hadoop, NoSQL, and other approaches. Their primary applications consist of marketing campaign optimization, customer churn and loyalty, upsell and cross-sell analysis, targeted offers, behavioral targeting, social media monitoring, sentiment analysis, brand monitoring, influencer analysis, customer experience optimization, content optimization, and placement optimization
  • Chief Information Officers use big data platforms for data discovery, data integration, business analytics, advanced analytics, exploratory data science.
  • Chief Operations Officers rely on big data for supply chain optimization, defect tracking, sensor monitoring, and smart grid, among other applications.
  • Chief Information Security Officer run security incident and event management, anti-fraud detection, and other sensitive applications on big data.
  • Chief Technology Officers do IT log analysis, event analytics, network analytics, and other systems monitoring, troubleshooting, and optimization applications on big data.
  • Chief Financial Officers run complex financial risk analysis and mitigation modeling exercises on big data platforms.

Q2. What are the most challenging problems you are facing when analysing Big Data?

James Kobielus: Searching for actionable intelligence in big data involves building and testing advanced-analytics models against large volumes of complex data that may be flowing in at high velocities.

At these scales, it’s easy to get overwhelmed in your analysis unless you automate the end-to-end processes of extracting intelligence at scale. Automation can also help control the cost of managing a growing volume of algorithmic models against ever expanding big-data collections. The key processes that need automating are data discovery, profiling, sampling, and preparation, as well as model building, scoring, and deployment.

Q3. How do you typically handle them?

James Kobielus: Automating the modeling process will boost data scientist productivity by an order of magnitude, freeing them from drudgery so that they can focus on the sorts of exploration, modeling, and visualization challenges that demand expert human judgment. Data scientists can accelerate their modeling automation initiatives by following these steps:

  • Virtualize access to data, metadata, rules, and predictive models, as well as to data integration, data warehousing, and advanced analytic applications through a BI semantic virtualization layer;
  • Unify access, governance, orchestration, automation, and administration across these resources within a service-oriented architecture;
  • Explore commercial tools that support maximum automation of model development, scoring, deployment, and execution;
  • Consolidate, accelerate, and deepen predictive analytics through integration into big-data platforms with scalable in-database execution; and
  • Migrate existing analytical data marts into multidomain big-data platforms with unified data, metadata, and model governance within service-oriented virtualization framework.

Q4. What are in your experience the typical mistakes made in large scale data projects?

James Kobielus: One of the most typical mistakes in large-scale data projects is losing sight of the biases that may skew the insights you extract.

Even if you accept that a data scientist’s integrity is rock-solid, intentions pure, skills stellar, and discipline rigorous, there’s no denying that bias may creep inadvertently into their work with big data. The biases may be minor or major, episodic or systematic, tangential or material to their findings and recommendations. Whatever their nature, the biases must be understood and corrected as fully as possible.

Here are some of the key sources of bias that may crop up in a data scientist’s work with big data:

  • Cognitive bias: This is the tendency to make skewed decisions based on pre-existing cognitive and heuristic factors–such as a misunderstanding of probabilities–rather than on the data and other hard evidence. You might say that the educated intuition that drives data science is rife with cognitive bias, but that’s not always a bad thing.
  • Selection bias: This is the tendency to skew your choice of data sources to those that may be most available, convenient, and cost-effective for your purposes, as opposed to being necessarily the most valid and relevant for your study. Clearly, data scientists do not have unlimited budgets, may operate under tight deadlines, and don’t use data for which they lack authorization. These constraints may introduce an unconscious bias in the big-data collections they are able to assemble.
  • Sampling bias: This is the tendency to skew the sampling of data sets toward subgroups of the population most relevant to the initial scope of a data-science project, thereby making it unlikely that you will uncover any meaningful correlations that may apply to other segments. Another source of sampling bias is “data dredging,” in which the data scientist uses regression techniques that may find correlations in samples but that may not be statistically significant in the wider population. Consequently, you’re likely to spuriously confirm your initial model for the segments that happen to make the sampling cut.
  • Modeling bias: Beyond the biases just discussed, this is the tendency to skew data-science models by starting with a biased set of project assumptions that drive selection of the wrong variables, the wrong data, the wrong algorithms, and the wrong metrics of fitness. In addition, overfitting of models to past data without regard for predictive lift is a common bias. Likewise, failure to score and iterate models in a timely fashion with fresh observational data also introduces model decay, hence bias.
  • Funding bias: This may be the most silent but pernicious bias in data-scientific studies of all sorts. It’s the unconscious tendency to skew all modeling assumptions, interpretations, data, and applications to favor the interests of the party–employer, customer, sponsor, etc.–that employs or otherwise financially supports the data-science initiative. Funding bias makes it highly unlikely that data scientists will uncover disruptive insights that will “break the rice bowl” in which they make their living.

Q5. How do you measure “success” when analysing data?

James Kobielus: You measure success in your ability to distill useful insights in a timely fashion from the data at your disposal.

Q6. What skills are required to be an effective Data Scientist?

James Kobielus: Data science’s learning curve is formidable. To a great degree, you will need a degree, or something substantially like it, to prove you’re committed to this career. You will need to submit yourself to a structured curriculum to certify you’ve spent the time, money and midnight oil necessary for mastering this demanding discipline.

Sure, there are run-of-the-mill degrees in data-science-related fields, and then there are uppercase, boldface, bragging-rights “DEGREES.” To some extent, it matters whether you get that old data-science sheepskin from a traditional university vs. an online school vs. a vendor-sponsored learning program. And it matters whether you only logged a year in the classroom vs. sacrificed a considerable portion of your life reaching for the golden ring of a Ph.D. And it certainly matters whether you simply skimmed the surface of old-school data science vs. pursued a deep specialization in a leading-edge advanced analytic discipline.

But what matters most to modern business isn’t that every data scientist has a big honking doctorate. What matters most is that a substantial body of personnel has a common grounding in core curriculum of skills, tools and approaches. Ideally, you want to build a team where diverse specialists with a shared foundation can collaborate productively.

Big data initiatives thrive if all data scientists have been trained and certified on a curriculum with the following foundation:

  • Paradigms and practices: Every data scientist should acquire a grounding in core concepts of data science, analytics and data management. They should gain a common understanding of the data science lifecycle, as well as the typical roles and responsibilities of data scientists in every phase. They should be instructed on the various role(s) of data scientists and how they work in teams and in conjunction with business domain experts and stakeholders. And they learn a standard approach for establishing, managing and operationalizing data science projects in the business.
  • Algorithms and modeling: Every data scientist should obtain a core understanding of linear algebra, basic statistics, linear and logistic regression, data mining, predictive modeling, cluster analysis, association rules, market basket analysis, decision trees, time-series analysis, forecasting, machine learning, Bayesian and Monte Carlo Statistics, matrix operations, sampling, text analytics, summarization, classification, primary components analysis, experimental design, unsupervised learning constrained optimization.
  • Tools and platforms: Every data scientist should master a core group of modeling, development and visualization tools used on your data science projects, as well as the platforms used for storage, execution, integration and governance of big data in your organization. Depending on your environment, and the extent to which data scientists work with both structured and unstructured data, this may involve some combination of data warehousing, Hadoop, stream computing, NoSQL and other platforms. It will probably also entail providing instruction in MapReduce, R and other new open-source development languages, in addition to SPSS, SAS and any other established tools.
  • Applications and outcomes: Every data scientist should learn the chief business applications of data science in your organization, as well as in how to work best with subject-domain experts. In many companies, data science focuses on marketing, customer service, next best offer, and other customer-centric applications. Often, these applications require that data scientists understand how to leverage customer data acquired from structured survey tools, sentiment analysis software, social media monitoring tools and other sources. It also essential that every data scientist gain an understanding of the key business outcomes–such as maximizing customer lifetime value–that should focus their modeling initiatives.

Classroom instruction is important, but a curriculum that is 100 percent devoted to reading books, taking tests and sitting through lectures is insufficient. Hands-on laboratory work is paramount for a truly well-rounded data scientist. Make sure that your data scientists acquire certifications and degrees that reflect them actually developing statistical models that use real data and address substantive business issues.

A business-oriented data-science curriculum should produce expert developers of statistical and predictive models. It should not degenerate into a program that produces analytics geeks with heads stuffed with theory but whose diplomas are only fit for hanging on the wall.

Q7. Hadoop vs. Spark: what are the pros and cons?

James Kobielus: Big data analytics infrastructures are growing more hybridized than ever. Every new technology—such as Hadoop, in-memory databases, and graph databases—finds its specific niche in terms of use cases, deployment modes, and applications for which it is best suited.

Even as Apache Spark pushes more deeply into big-data environments, it won’t substantially change this trend. Yes, of course Spark is on the fast track to ubiquity in big-data analytics. This is especially true for the next generation of machine-learning applications that feed on growing in-memory pools and require low-latency distributed computations for streaming and graph analytics. But those use cases aren’t the sum total of big-data analytics and never will be.

As we all grow more infatuated with Spark, it’s important to continually remind ourselves of what it’s not suitable for. If, for example, one considers all the critical data management, integration, and preparation tasks that must be performed prior to modeling in Spark, it’s clear that these will not be executed in any of the Spark engines (Spark SQL, Spark Streaming, GraphX). Instead, they’ll be carried out in the data platforms and elastic clusters (HDFS, Cassandra, HBase, Mesos, cloud services, etc.) upon which those engines run. Likewise, you’d be hardpressed to find anyone who’s seriously considering Spark in isolation for data warehousing, data governance, master data management, or operational business intelligence.

Above all else, Spark is the new power tool for data scientists who are pushing boundaries in the emerging era of in-memory big data analytics in low-latency scenarios of all types. Spark is proving its value as a development tool for the new generation of data scientists building the in-memory statistical models upon which it all will depend.

Let’s not fall into the delusion that everything is converging toward Spark, as if it were the ravenous maw that will devour every other big-data analytics tool and platform. Spark is just another approach that’s being fitted to and optimized for specific purposes.

And let’s resist the hype that treats Spark as Hadoop’s “successor.” This implies that Hadoop and other big-data approaches are “legacy,” rather than what they are, which is foundational. For example, no one is seriously considering doing “data lakes,” “data reservoirs,” or “data refineries” on anything but Hadoop or NoSQL.


James Kobielus is an industry veteran and serves as IBM Big Data Evangelist; Senior Program Director for Product Marketing in Big Data Analytics; and Team Lead, Technical Marketing, IBM Big Data & Analytics Hub. He spearheads thought leadership activities across the IBM Analytics solution portfolio. He has spoken at such leading industry events as IBM Insight, Hadoop Summit, and Strata. He has published several business technology books and is a very popular provider of original commentary on blogs and many social media.


–  Master of Information and Data Science,  UC Berkeley School of Information.

– MS in Data Science, NYU Center for Data Science.

– Free data science curriculum, kdnuggets.com

Data Science | Coursera

– Master of Science in Data Science – Data Science Institute

Data Mining and Applications Graduate Certificate, Stanford

The European Data Science Academy (EDSA) designs curricula for data science training and data science education across the European Union (EU).

-The EDISON project will focus on activities to establish the new profession of ‘Data Scientist’, following the emergence of Data Science technologies (also referred to as Data Intensive or Big Data technologies) which changes the way research is done, how scientists think and how the research data are used and shared. This includes definition of the required skills, competences framework/profile, corresponding Body Of Knowledge and model curriculum. It will develop a sustainability/business model to ensure a sustainable increase of Data Scientists, graduated from universities and trained by other professional education and training institutions in Europe. 
EDISON will facilitate the establishment of a Data Science education and training infrastructure at major European universities by promoting experience of ‘champion’ universities involving them into coordinated development and implementation of the model curriculum and creation of cooperative educational and training infrastructure.

Related Posts

– RIP Big Data, By Carl Olofson, Research Vice President, Data Management Software Research, IDC. ODBMS.org, January  2016

Open Source Software and IBM’s Big Data platform. By Cynthia M. Saracco, senior solutions architect at IBM’s Silicon Valley Laboratory. ODBMS.org, April 2016.

Looking back at Big Data in 2015, By Cynthia M. Saracco, IBM Senior Solution Architect, ODBMS.org. November 2015

–  Heuristics for a Data Scientist: A common sense approach. BY Silvia Dassiè, Data Scientist at Ryanair. ODBMS.org, December 2015

The rise of immutable data stores. By Alan Morrison, Senior Manager, PwC Center for technology and innovation. ODBMS.org. October 2015

Follow us on Twitter: @odbmsorg


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On the Internet of Things. Interview with Colin Mahony http://www.odbms.org/blog/2016/03/on-the-internet-of-things-interview-with-colin-mahony/ http://www.odbms.org/blog/2016/03/on-the-internet-of-things-interview-with-colin-mahony/#comments Mon, 14 Mar 2016 08:45:56 +0000 http://www.odbms.org/blog/?p=4101

“Frankly, manufacturers are terrified to flood their data centers with these unprecedented volumes of sensor and network data.”– Colin Mahony

I have interviewed Colin Mahony, SVP & General Manager, HPE Big Data Platform. Topics of the interview are: The challenges of the Internet of Things, the opportunities for Data Analytics, the positioning of HPE Vertica and HPE Cloud Strategy.


Q1. Gartner says 6.4 billion connected “things” will be in use in 2016, up 30 percent from 2015.  How do you see the global Internet of Things (IoT) market developing in the next years?

Colin Mahony: As manufacturers connect more of their “things,” they have an increased need for analytics to derive insight from massive volumes of sensor or machine data. I see these manufacturers, particularly manufacturers of commodity equipment, with a need to provide more value-added services based on their ability to provide higher levels of service and overall customer satisfaction. Data analytics platforms are key to making that happen. Also, we could see entirely new analytical applications emerge, driven by what consumers want to know about their devices and combine that data with, say, their exercise regimens, health vitals, social activities, and even driving behavior, for full personal insight.
Ultimately, the Internet of Things will drive a need for the Analyzer of Things, and that is our mission.

Q2. What Challenges and Opportunities bring the Internet of Things (IoT)? 

Colin Mahony: Frankly, manufacturers are terrified to flood their data centers with these unprecedented volumes of sensor and network data. The reason? Traditional data warehouses were designed well before the Internet of Things, or, at least before OT (operational technology) like medical devices, industrial equipment, cars, and more were connected to the Internet. So, having an analytical platform to provide the scale and performance required to handle these volumes is important, but customers are taking more of a two- or three-tier approach that involves some sort of analytical processing at the edge before data is sent to an analytical data store. Apache Kafka is also becoming an important tier in this architecture, serving as a message bus, to collect and push that data from the edge in streams to the appropriate database, CRM system, or analytical platform for, as an example, correlation of fault data over months or even years to predict and prevent part failure and optimize inventory levels.

Q3. Big Data: In your opinion, what are the current main demands/needs in the market?

Colin Mahony: All organizations want – and need – to become data-driven organizations. I mean, who wants to make such critical decisions based on half answers and anecdotal data? That said, traditional companies with data stores and systems going back 30-40 years don’t have the same level playing field as the next market disruptor that just received their series B funding and only knows that analytics is the life blood of their business and all their critical decisions.
The good news is that whether you are a 100-year old insurance company or the next Uber or Facebook, you can become a data-driven organization by taking an open platform approach that uses the best tool for the job and can incorporate emerging technologies like Kafka and Spark without having to bolt on or buy all of that technology from a single vendor and get locked in.  Understanding the difference between an open platform with a rich ecosystem and open source software as one very important part of that ecosystem has been a differentiator for our customers.

Beyond technology, we have customers that establish analytical centers of excellence that actually work with the data consumers – often business analysts – that run ad-hoc queries using their preferred data visualization tool to get the insight need for their business unit or department. If the data analysts struggle, then this center of excellence, which happens to report up through IT, collaborates with them to understand and help them get to the analytical insight – rather than simply halting the queries with no guidance on how to improve.

Q4. How do you embed analytics and why is it useful? 

Colin Mahony: OEM software vendors, particularly, see the value of embedding analytics in their commercial software products or software as a service (SaaS) offerings.  They profit by creating analytic data management features or entirely new applications that put customers on a faster path to better, data-driven decision making. Offering such analytics capabilities enables them to not only keep a larger share of their customer’s budget, but at the same time greatly improve customer satisfaction. To offer such capabilities, many embedded software providers are attempting unorthodox fixes with row-oriented OLTP databases, document stores, and Hadoop variations that were never designed for heavy analytic workloads at the volume, velocity, and variety of today’s enterprise. Alternatively, some companies are attempting to build their own big data management systems. But such custom database solutions can take thousands of hours of research and development, require specialized support and training, and may not be as adaptable to continuous enhancement as a pure-play analytics platform. Both approaches are costly and often outside the core competency of businesses that are looking to bring solutions to market quickly.

Because it’s specifically designed for analytic workloads, HPE Vertica is quite different from other commercial alternatives. Vertica differs from OLTP DBMS and proprietary appliances (which typically embed row-store DBMSs) by grouping data together on disk by column rather than by row (that is, so that the next piece of data read off disk is the next attribute in a column, not the next attribute in a row). This enables Vertica to read only the columns referenced by the query, instead of scanning the whole table as row-oriented databases must do. This speeds up query processing dramatically by reducing disk I/O.

You’ll find Vertica as the core analytical engine behind some popular products, including Lancope, Empirix, Good Data, and others as well as many HPE offerings like HPE Operations Analytics, HPE Application Defender, and HPE App Pulse Mobile, and more.

Q5. How do you make a decision when it is more appropriate to “consume and deploy” Big Data on premise, in the cloud, on demand and on Hadoop?

Colin Mahony: The best part is that you don’t need to choose with HPE. Unlike most emerging data warehouses as a service where your data is trapped in their databases when your priorities or IT policies change, HPE offers the most complete range of deployment and consumption models. If you want to spin up your analytical initiative on the cloud for a proof-of-concept or during the holiday shopping season for e-retailers, you can do that easily with HPE Vertica OnDemand.
If your organization finds that due to security or confidentiality or privacy concerns you need to bring your analytical initiative back in house, then you can use HPE Vertica Enterprise on-premises without losing any customizations or disruption to your business. Have petabyte volumes of largely unstructured data where the value is unknown? Use HPE Vertica for SQL on Hadoop, deployed natively on your Hadoop cluster, regardless of the distribution you have chosen. Each consumption model, available in the cloud, on-premise, on-demand, or using reference architectures for HPE servers, is available to you with that same trusted underlying core.

Q6. What are the new class of infrastructures called “composable”? Are they relevant for Big Data?

Colin Mahony: HPE believes that a new architecture is needed for Big Data – one that is designed to power innovation and value creation for the new breed of applications while running traditional workloads more efficiently.
We call this new architectural approach Composable Infrastructure. HPE has a well-established track record of infrastructure innovation and success. HPE Converged Infrastructure, software-defined management, and hyper-converged systems have consistently proven to reduce costs and increase operational efficiency by eliminating silos and freeing available compute, storage, and networking resources. Building on our converged infrastructure knowledge and experience, we have designed a new architecture that can meet the growing demands for a faster, more open, and continuous infrastructure.

Q7. What is HPE Cloud Strategy? 

Colin Mahony: Hybrid cloud adoption is continuing to grow at a rapid rate and a majority of our customers recognize that they simply can’t achieve the full measure of their business goals by consuming only one kind of cloud.
HPE Helion not only offers private cloud deployments and managed private cloud services, but we have created the HPE Helion Network, a global ecosystem of service providers, ISVs, and VARs dedicated to delivering open standards-based hybrid cloud services to enterprise customers. Through our ecosystem, our customers gain access to an expanded set of cloud services and improve their abilities to meet country-specific data regulations.

In addition to the private cloud offerings, we have a strategic and close alliance with Microsoft Azure, which enables many of our offerings, including Haven OnDemand, in the public cloud. We also work closely with Amazon because our strategy is not to limit our customers, but to ensure that they have the choices they need and the services and support they can depend upon.

Q8. What are the advantages of an offering like Vertica in this space?

Colin Mahony: More and more companies are exploring the possibility of moving their data analytics operations to the cloud. We offer HPE Vertica OnDemand, our data warehouse as a service, for organizations that need high-performance enterprise class data analytics for all of their data to make better business decisions now. Built by design to drastically improve query performance over traditional relational database systems, HPE Vertica OnDemand is engineered from the same technology that powers the HPE Vertica Analytics Platform. For organizations that want to select Amazon hardware and still maintain the control over the installation, configuration, and overall maintenance of Vertica for ultimate performance and control, we offer Vertica AMI (Amazon Machine Image). The Vertica AMI is a bring-your-own-license model that is ideal for organizations that want the same experience as on-premise installations, only without procuring and setting up hardware. Regardless of which deployment model to choose, we have you covered for “on demand” or “enterprise cloud” options.

Q9. What is HPE Vertica Community Edition?

Colin Mahony: We have had tens of thousands of downloads of the HPE Vertica Community Edition, a freemium edition of HPE Vertica with all of the core features and functionality that you experience with our core enterprise offering. It’s completely free for up to 1 TB of data storage across three nodes. Companies of all sizes prefer the Community Edition to download, install, set-up, and configure Vertica very quickly on x86 hardware or use our Amazon Machine Image (AMI) for a bring-your-own-license approach to the cloud.

Q10. Can you tell us how Kiva.org, a non-profit organization, uses on-demand cloud analytics to leverage the internet and a worldwide network of microfinance institutions to help fight poverty? 

Colin Mahony: HPE is a major supporter of Kiva.org, a non-profit organization with a mission to connect people through lending to alleviate poverty. Kiva.org uses the internet and a worldwide network of microfinance institutions to enable individuals lend as little as $25 to help create opportunity around the world. When the opportunity arose to help support Kiva.org with an analytical platform to further the cause, we jumped at the opportunity. Kiva.org relies on Vertica OnDemand to reduce capital costs, leverage the SaaS delivery model to adapt more quickly to changing business requirements, and work with over a million lenders, hundreds of field partners and volunteers, across the world. To see a recorded Webinar with HPE and Kiva.org, see here.

Qx Anything else you wish to add?

Colin Mahony: We appreciate the opportunity to share the features and benefits of HPE Vertica as well as the bright market outlook for data-driven organizations. However, I always recommend that any organization that is struggling with how to get started with their analytics initiative to speak and meet with peers to learn best practices and avoid potential pitfalls. The best way to do that, in my opinion, is to visit with the more than 1,000 Big Data experts in Boston from August 29 – September 1st at the HPE Big Data Conference. Click here to learn more and join us for 40+ technical deep-dive sessions.


Colin Mahony, SVP & General Manager, HPE Big Data Platform

Colin Mahony leads the Hewlett Packard Enterprise Big Data Platform business group, which is responsible for the industry leading Vertica Advanced Analytics portfolio, the IDOL Enterprise software that provides context and analysis of unstructured data, and Haven OnDemand, a platform for developers to leverage APIs and on demand services for their applications.
In 2011, Colin joined Hewlett Packard as part of the highly successful acquisition of Vertica, and took on the responsibility of VP and General Manager for HP Vertica, where he guided the business to remarkable annual growth and recognized industry leadership. Colin brings a unique combination of technical knowledge, market intelligence, customer relationships, and strategic partnerships to one of the fastest growing and most exciting segments of HP Software.

Prior to Vertica, Colin was a Vice President at Bessemer Venture Partners focused on investments primarily in enterprise software, telecommunications, and digital media. He established a great network and reputation for assisting in the creation and ongoing operations of companies through his knowledge of technology, markets and general management in both small startups and larger companies. Prior to Bessemer, Colin worked at Lazard Technology Partners in a similar investor capacity.

Prior to his venture capital experience, Colin was a Senior Analyst at the Yankee Group serving as an industry analyst and consultant covering databases, BI, middleware, application servers and ERP systems. Colin helped build the ERP and Internet Computing Strategies practice at Yankee in the late nineties.

Colin earned an M.B.A. from Harvard Business School and a bachelor’s degrees in Economics with a minor in Computer Science from Georgetown University.  He is an active volunteer with Big Brothers Big Sisters of Massachusetts Bay and the Joey Fund for Cystic Fibrosis.


What’s in store for Big Data analytics in 2016, Steve Sarsfield, Hewlett Packard Enterprise. ODBMS.org, 3 FEB, 2016

What’s New in Vertica 7.2?: Apache Kafka Integration!, HPE, last edited February 2, 2016

Gartner Says 6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015, Press release, November 10, 2015

The Benefits of HP Vertica for SQL on Hadoop, HPE, July 13, 2015

Uplevel Big Data Analytics with Graph in Vertica – Part 5: Putting graph to work for your business , Walter Maguire, Chief Field Technologist, HP Big Data Group, ODBMS.org, 2 Nov, 2015

HP Distributed R ,ODBMS.org,  19 FEB, 2015.

Understanding ROS and WOS: A Hybrid Data Storage Model, HPE, October 7, 2015

Related Posts

On Big Data Analytics. Interview with Shilpa LawandeSource: ODBMS Industry Watch, Published on December 10, 2015

On HP Distributed R. Interview with Walter Maguire and Indrajit RoySource: ODBMS Industry Watch, Published on April 9, 2015

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Orleans, the technology behind Xbox Halo4 and Halo5. Interview with Phil Bernstein http://www.odbms.org/blog/2016/02/orleans-the-technology-behind-xbox-halo4-and-halo5-interview-with-phil-bernstein/ http://www.odbms.org/blog/2016/02/orleans-the-technology-behind-xbox-halo4-and-halo5-interview-with-phil-bernstein/#comments Tue, 09 Feb 2016 19:06:44 +0000 http://www.odbms.org/blog/?p=4081

“Orleans is an open-source programming framework for .NET that simplifies the development of distributed applications, that is, ones that run on many servers in a datacenter.”– Phil Bernstein.

I have interviewed, Phil Bernstein,a well known data base researcher and Distinguished Scientist at Microsoft Research, where he has worked for over 20 years. We discussed his latest project “Orleans”.


Q1. With the project “Orleans” you and your team invented the “Virtual Actor abstraction”. What is it? 

Phil Bernstein: Orleans is an open-source programming framework for .NET that simplifies the development of distributed applications, that is, ones that run on many servers in a datacenter. In Orleans, objects are actors, by which we mean that they don’t share memory.

In Orleans, actors are virtual in the same sense as virtual memory: an object is activated on demand, i.e. when one of its methods is invoked. If an object is already active when it’s invoked, the Orleans runtime will use its object directory to find the object and invoke it. If the runtime determines that the object isn’t active, the runtime will choose a server on which to activate the object, invoke the object’s constructor on that server to load its state, invoke the method, and update the object directory so it can direct future calls to the object.

Conversely, an object is deactivated when it hasn’t been invoked for some time. In that case, the runtime calls the object’s deactivate method, which does whatever cleanup is needed before freeing up the object’s runtime resources.

Q2. How is it possible to build distributed interactive applications, without the need to learn complex programming patterns? 

Phil Bernstein:  The virtual actor model hides distribution from the developer. You write code as if your program runs on one machine. The Orleans runtime is responsible for distributing objects across servers, which is something that doesn’t affect the program logic. Of course, there are performance and fault tolerance implications of distribution.
But Orleans is able to hide them too.

Q3. Building interactive services that are scalable and reliable is hard. How do you ensure that Orleans applications scale-up and are reliable?

Phil Bernstein:  The biggest impediment to scaling out an app across servers is to ensure no server is a bottleneck. Orleans does this by evenly distributing the objects across servers. This automatically balances the load.

As for reliability, the virtual actor model makes this automatic. If a server fails, then of course all of the objects that were active on that server are gone. No problem. The Orleans runtime detects the server failure and knows which objects were active on the failed server. So the next time any of those objects is invoked, it takes its usual course of action, that is, it chooses a server on which to activate the object, loads the object, and invokes it.

Q4. What about the object’s state? Doesn’t that disappear when its server fails?

Phil Bernstein:  Yes, of course all of the object’s main memory state is lost. It’s up to the object’s methods to save object state persistently, typically just before returning from a method that modifies the object’s state.

Q5. Is this transactional?

Phil Bernstein:  No, not yet. We’re working on adding a transaction mechanism. Coming soon.

Q6. Can you give us an example of an Orleans application?

Phil Bernstein:  Orleans is used for developing large-scale on-line games. For example, all of the cloud services for Halo 4 and Halo 5, the popular Xbox games, run on Orleans. Example object types are players, game consoles, game instances, weapons caches, and leaderboards. Orleans is also used for Internet of Things, communications, and telemetry applications. All of these applications are naturally actor-oriented, so they fit well with the Orleans programming model.

Q7. Why does the traditional three-tier architecture with stateless front-ends, stateless middle tier and a storage layer have limited scalability? 

Phil Bernstein:  The usual bottleneck is the storage layer. To solve this, developers add a middle tier to cache some state and thereby reduce the storage load. However, this middle tier loses the concurrency control semantics of storage, and now you have the hard problem of distributed cache invalidation. To enforce storage semantics, Orleans makes it trivial to express cached items as objects. And to avoid concurrency control problems, it routes requests to a single instance of each object, which is ordinarily single-threaded.

Also, a middle-tier cache does data shipping to the storage servers, which can be inefficient. With Orleans, you have an object-oriented cache and do function shipping instead.

Q8. How does Orleans differ from other Actor platforms such as Erlang and Akka? 

Phil Bernstein: In Erlang and Akka, the developer controls actor lifecycle. You explicitly create an actor and choose the server on which it’s activated. Fixing the actor’s location at creation time prevents automating load balancing, actor migration, and server failure handling. For example, if an actor fails, you need code to catch the exception and resurrect the actor on another server. In Orleans, this is all automatic.

Another difference is the communications model. Orleans uses asynchronous RPC’s. Erlang and Akka use one-way messages.

Q9. Database people sometimes focus exclusively on the data model and query language, and don’t consider the problem of writing a scalable application on top of the database. How is Orleans addressing this issue?

Phil Bernstein:  In a database-centric view, an app is a set of stored procedures with a stateless front-end and possibly a middle-tier cache. To scale out the app with this design, you need to partition the database into finer slices every time you want to add servers. By contrast, if your app runs on servers that are separate from the database, as it does with Orleans, you can add servers to scale out the app without scaling out the storage. This is easier, more flexible, and less expensive. For example, you can run with more app servers during the day when there’s heavier usage and fewer servers at night when the workload dies down. This is usually infeasible at the database server layer, since it would require migrating parts of the database twice a day.

Q10. Why did you transfer the core Orleans technology to 343 Industries ?

Phil Bernstein:  Orleans was developed in Microsoft Research starting in 2009. Like any research project, after several years of use in production, it was time to move it into a product group, which can better afford the resources to support it. Initially, that was 343 Industries, the biggest Orleans user, which ships the Halo game. After Halo 5 shipped, the Orleans group moved to the parent organization, Microsoft Game Studios, which provides technology to Halo and many other Xbox games.

In Microsoft Research, we are still working on Orleans technology and collaborate closely with the product group. For example, we recently published code to support geo-distributed applications on Orleans, and we’re currently working on adding a transaction mechanism.

Q11. The core Orleans technology was also made available as open source in January 2015. Are developers actively contributing to this? 

Phil Bernstein: Yes, there is a lot of activity, with contributions from developers both inside and outside Microsoft. You can see the numbers on GitHub – roughly 25 active contributors and over 25 more occasional contributors – with fully-tested releases published every couple of months. After the core .NET runtime and Roslyn compiler projects, Orleans is the next most popular .NET Foundation project on GitHub.


Phil Bernstein is a Distinguished Scientist at Microsoft Research, where he has worked for over 20 years. Before Microsoft, he was a product architect and researcher at Digital Equipment Corp. and a professor at Harvard University. He has published over 150 papers and two books on the theory and implementation of database systems, especially on transaction processing and data integration, which are still the major areas of his work. He is an ACM Fellow, a winner of the ACM SIGMOD Innovations Award, a member of the Washington State Academy of Sciences and a member of the U.S. National Academy of Engineering. He received a B.S. degree from Cornell and M.Sc. and Ph.D. from University of Toronto.


Microsoft Research homepage for Orleans

Orleans code on GitHub

Orleans documentation

Related Posts

On the Industrial Internet of Things. Interview with Leon Guzenda ODBMS Industry Watch, Published on 2016-01-28

Challenges and Opportunities of The Internet of Things. Interview with Steve Cellini ODBMS Industry Watch, Published on 2015-10-07

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On Hadoop and Big Data. Interview with John Leach http://www.odbms.org/blog/2015/07/on-hadoop-and-big-data-interview-with-john-leach/ http://www.odbms.org/blog/2015/07/on-hadoop-and-big-data-interview-with-john-leach/#comments Mon, 13 Jul 2015 08:32:52 +0000 http://www.odbms.org/blog/?p=3941

“One common struggle for data-driven enterprises is managing unnecessarily complicated data workflows with bloated ETL pipelines and a lack of native system integration.”– John Leach

I have interviewed John Leach, CTO & Cofounder Splice Machine.  Main topics of the interview are Hadoop, Big Data integration and what Splice Machine has to offer in this space.  Monte Zweben, CEO of Splice Machine also contributed to the interview.


Q1. What are the Top Ten Pitfalls to Avoid in a SQL-on-Hadoop Implementation?

John Leach, Monte Zweben:
1. Individual record lookups. Most SQL-on-Hadoop engines are designed for full table scans in analytics, but tend to be too slow for the individual record lookups and ranges scan used by operational applications.
2. Dirty Data. Dirty data is a problem for any system, but it is compounded in Big Data, often resulting in bad reports and delays to reload an entire data set.
3. Sharding. It can be difficult to know what key to distribute data and the right shard size. This results in slow queries, especially for large joins or aggregations.
4. Hotspotting. This happens when data becomes too concentrated in a few nodes, especially for time series data. The impact is slow queries and poor parallelization.
5. SQL coverage. Limited SQL dialects will make it so you can’t run queries to meet business needs. You’ll want to make sure you do your homework. Compile the list of toughest queries and test.
6. Concurrency. Low concurrency can result in the inability to power real-time apps, handle many users, support many input sources, and deliver reports as updates happen.
7. Columnar. Not all columnar solutions are created equally. Besides columnar storage, there are many other optimizations, such as vectorization and run length encoding that can have a big impact on analytic performance. If your OLAP queries run slower, common with large joins and aggregations, this will result in poor productivity. Queries may take minutes or hours instead of seconds. On the flip-side is using columnar when you need concurrency and real-time.
8. Node Sizing. Do your homework and profile your workload. Choosing the wrong node size (e.g., CPU cores, memory) can negatively impact price/performance and create performance bottlenecks.
9. Brittle ETL on Hadoop. With many SQL-on-Hadoop solutions being unable to provide update or delete capabilities without a full data reload, this can cause a very brittle ETL that will require restarting your ETL pipeline because of errors or data quality issues. The result is a missed ETL window and delayed reports to business users.
10. Cost-Based Optimizer. A cost-based optimizer improves performance by selecting the right join strategy, the right index, and the right ordering. Some SQL-on-Hadoop engines have no cost-based optimizer or relatively immature ones that can result in poor performance and poor productivity, as well as manual tuning by DBAs.

Q2. In your experience, what are the most common problems in Big Data integration?

John Leach, Monte Zweben: Providing users access to data in a fashion they can understand and at the moment they need it, while ensuring quality and security, can be incredibly challenging.

The volume and velocity of data that businesses are churning out, along with the variety of different sources, can pose many issues.

One common struggle for data-driven enterprises is managing unnecessarily complicated data workflows with bloated ETL pipelines and a lack of native system integration. Businesses may also find their skill sets, workload, and budgets over-stretched by the need to manage terabytes or petabytes of structured and unstructured data in a way that delivers genuine value to business users.

When data is siloed and there is no solution put into place, businesses can’t access the real-time insights they need to make the best decisions for their business. Performance goes down, headaches abound and cost goes way up, all in the effort to manage the data. That’s why a Big Data integration solution is a prerequisite for getting the best performance and the most real-time insights, at the lowest cost.

Q3. What are the capabilities of Hadoop beyond data storage?

John Leach, Monte Zweben: Hadoop has a very broad range of capabilities and tools:

Oozie for workflow
Pig for scripting
Mahout or SparkML for machine learning
Kafka and Storm for streaming
Flume and Sqoop for integration
Hive, Impala, Spark, and Drill for SQL analytic querying
HBase for NoSQL
Splice Machine for operational, transactional RDBMS

Q4. What programming skills are required to handle application development around Big Data platforms like Hadoop?

John Leach, Monte Zweben: To handle application development on Hadoop, individuals have choices to go raw Hadoop or SQL-on-Hadoop. When going the SQL route, very little new skills are required and developers can open connections to an RDBMS on Hadoop just like they used to do on Oracle, DB2, SQLServer, or Teradata. Raw HAdoop application developers should know their way around the core components of the Hadoop stack–such as HDFS, MapReduce, Kafaka, Storm, Oozie, Hive, Pig, HBase, and YARN. They should also be proficient in Java.

Q5. What are the current challenges for real-time application deployment on Hadoop?

John Leach, Monte Zweben: When we talk about real-time at Splice Machine, we’re focused on applications that require not only real-time responses to queries, but also real-time database updates from a variety of data sources. The former is not all that uncommon on Hadoop; the latter is nearly impossible for most Hadoop-based systems.

Deploying real-time applications on Hadoop is really a function of moving Hadoop beyond its batch processing roots to be able to handle real-time database updates with high concurrency and transactional integrity. We harness HBase along with a lockless snapshot isolation design to provide full ACID transactions across rows and tables.

This technology enables Splice Machine to execute the high concurrency of transactions required by real-time applications.

Q6. What is special about Splice Machine auto-sharding replication and failover technology?

John Leach, Monte Zweben: As part of its automatic auto-sharding, HBase horizontally partitions or splits each table into smaller chunks or shards that are distributed across multiple servers. Using the inherent failover and replication capabilities of HBase and Hadoop, Splice Machine can support applications that demand high availability.

HBase co-processors are used to embed Splice Machine in each distributed HBase region (i.e., data shard). This enables Splice Machine to achieve massive parallelization by pushing the computation down to each distributed data shard without any overhead of MapReduce.

Q7. How difficult is it for customers to migrate from legacy databases to Splice Machine?

John Leach, Monte Zweben: Splice Machine offers a variety of services to help businesses efficiently deploy the Splice Machine database and derive maximum value from their investment. These services include both implementation consulting and educational offerings delivered by our expert team.

Splice Machine has designed a Safe Journey program to significantly ease the effort and risk for companies migrating to a Splice Machine database. The Safe Journey program includes a proven methodology that helps choose the right workloads to migrate, implements risk-mitigation best practices, and includes commercial tools that automate most of the PL/SQL conversion process.

This is not to suggest that all legacy databases will convert to a Hadoop RDBMS.
The best candidates will typically have over 1TB of data, which often leads to cost and scaling issues in legacy databases.

Q8. You have recently announced partnership with Talend, mrc (michaels, ross & cole ltd.) and RedPoint Global. Why Talend, mrc, and RedPoint Global? What is the strategic meaning of these partnerships for Splice Machine?

John Leach, Monte Zweben: Our uptick in recent partnerships demonstrates the tremendous progress our team has made over the past year. We have been working relentlessly to develop the Splice Machine Hadoop RDBMS into a fully enterprise-ready database that can replace legacy database systems.

The demand for programming talent to handle application development is growing faster than the supply of skilled talent, especially around newer platforms like Hadoop. We partnered with mrc to give businesses a solution that can speed real-time application deployment on Hadoop with the staff and tools they currently have, while also offering future-proof applications over a database that scales to meet increasing data demands.

We partnered with Talend to bring our customers the benefit of two different approaches for managing data integration affordable and at scale. Talend’s rich capabilities including drag and drop user interface, and adaptable platform allow for increased productivity and streamlined testing for faster deployment of web, mobile, OLTP or Internet of Things applications.

And finally, we integrated and certified our Hadoop RDBMS on RedPoint’s Convergent Marketing Platform™ to create a new breed of solution for marketers. With cost-efficient database scale-out and real-time cross-channel execution, the solution enables enterprises to future-proof their marketing technology investment through affordable access to all their data (social, mobile, click streams, website behaviors, etc.) across a proliferating and ever-changing list of channels. Furthermore, it complements any existing Hadoop deployment, including those on the Cloudera, MapR and Hortonworks distributions.

Q9. How is Splice Machine working with Hadoop distribution partners –such as MapR, Hortonworks and Cloudera?

John Leach, Monte Zweben: Since Splice Machine does not modify HBase, it can be used with any standard Hadoop distribution that includes HBase, including Cloudera, MapR and Hortonworks. Splice Machine enables enterprises using these three companies to tap into real-time updates with transactional integrity, an important feature for companies looking to become real-time, data-driven businesses.

In 2013, Splice Machine partnered with MapR to enable companies to use the MapR distribution for Hadoop to build their real time, SQL-on-Hadoop applications. In 2014, we joined the Cloudera Connect Partner Program, after certifying on CDH 5. We are working closely with Cloudera to maximize the potential of its full suite of Hadoop-powered software and our unique approach to real-time Hadoop.

That same year, we joined Hortonworks Technology Partner program. This enabled our users to harness innovations in management, provisioning and security for HDP deployments. For HDP users, Splice Machine enables them to build applications that use ANSI-standard SQL and support real-time updates with transactional integrity, allowing Hadoop to be used in both OLTP and OLAP applications.

Earlier this year, we were excited to achieve Hortonworks® Data Platform (HDP™) Certification. With the HDP certification, our customers can leverage the pre-built and validated integrations between leading enterprise technologies and the Hortonworks Data Platform, the industry’s only 100-percent open source Hadoop distribution, to simplify and accelerate their Splice Machine and Hadoop deployments.

Q10 What are the challenges of running online transaction processing on Hadoop?

John Leach, Monte Zweben: With its heritage as a batch processing system, Hadoop does not provide the transaction support required by online transaction processing. Transaction support can be tricky enough to implement for shared-disk RDBMSs such as Oracle, but it becomes far more difficult to implement in distributed environments such as Hadoop. A distributed transactional model requires high-levels of coordination across a cluster with too much overhead, while simultaneously providing high performance for a high concurrency of small read and writes, high-speed ingest, and massive bulk loads. We prove this by being able to run the TPC-C benchmark at scale.

Splice Machine met those requirements by using distributed snap isolation, a Multi-Version Concurrency Control model that delivers lockless, and high-concurrency transactional support. Splice Machine extended research from Google’s Percolator project, Yahoo Lab’s OMID project, and the University of Waterloo’s HBaseSI project to develop its own patent-pending, distributed transactions.


John LeachCTO & Cofounder Splice Machine
With over 15 years of software experience under his belt, John’s expertise in analytics and BI drives his role as Chief Technology Officer. Prior to Splice Machine, John founded Incite Retail in June 2008 and led the company’s strategy and development efforts. At Incite Retail, he built custom Big Data systems (leveraging HBase and Hadoop) for Fortune 500 companies.
Prior to Incite Retail, he ran the business intelligence practice at Blue Martini Software and built strategic partnerships with integration partners. John was a key subject matter expert for Blue Martini Software in many strategic implementations across the world. His focus at Blue Martini was helping clients incorporate decision support knowledge into their current business processes utilizing advanced algorithms and machine learning.
John received dual bachelor’s degrees in biomedical and mechanical engineering from Washington University in Saint Louis. Leach currently is the organizer for the Saint Louis Hadoop Users Group and is active in the Washington University Elliot Society.

Monte Zweben – CEO & Cofounder Splice Machine
A technology industry veteran, Monte’s early career was spent with the NASA Ames Research Center as the Deputy Chief of the Artificial Intelligence Branch, where he won the prestigious Space Act Award for his work on the Space Shuttle program.
Monte then founded and was the Chairman and CEO of Red Pepper Software, a leading supply chain optimization company, which merged in 1996 with PeopleSoft, where he was VP and General Manager, Manufacturing Business Unit.

In 1998, Monte was the founder and CEO of Blue Martini Software – the leader in e-commerce and multi-channel systems for retailers. Blue Martini went public on NASDAQ in one of the most successful IPOs of 2000, and is now part of JDA.
Following Blue Martini, he was the chairman of SeeSaw Networks, a digital, place-based media company. Monte is also the co-author of Intelligent Scheduling and has published articles in the Harvard Business Review and various computer science journals and conference proceedings.

Zweben currently serves on the Board of Directors of Rocket Fuel Inc. as well as the Dean’s Advisory Board for Carnegie-Mellon’s School of Computer Science.



– Splice Machine resource page, ODBMS.org

Related Posts

Common misconceptions about SQL on Hadoop. By Cynthia M. Saracco, ODBMS.org, July 2015

– SQL over Hadoop: Performance isn’t everything… By Simon Harris, ODBMS.org, March 2015

– Archiving Everything with Hadoop. By Mark Cusack, ODBMS.org. December 2014.

–  On Hadoop RDBMS. Interview with Monte Zweben. ODBMS Industry Watch  November 2, 2014

– AsterixDB: Better than Hadoop? Interview with Mike Carey, ODBMS Industry Watch, October 22, 2014


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