Big Data: Three questions to Pivotal.
“We are investing heavily in bringing SQL as the standard interface for accessing in real time (GemFire XD) and interactive (HAWQ) response times enabling enterprises to leverage their existing workforce for Hadoop processing.”–Susheel Kaushik.
I start this new year with a new series of short interviews to leading vendors of Big Data technologies. I call them “Big Data: three questions to“. The second of such interviews is with Susheel Kaushik, Senior Director, Product Management at Pivotal.
Q1. What is your current products offering?
Susheel Kaushik: Pivotal suite of products converge Apps, Data and Analytics for the enterprise customers.
Industry leading application frameworks and runtimes focused on enterprise needs. Pivotal App frameworks provide a rich set of product components that enables rapid application development including support for messaging, database services and robust analytic and visualization instrumentation.
• Pivotal tc Server: Lean, Powerful Apache Tomcat compatible application server that maximizes performance, scales easily, and minimizes cost and overhead.
• Pivotal Web Server: High Performance, Scalable and Secure HTTP server.
• Pivotal Rabbit MQ: Fast and dependable message server that supports a wide range of use cases including reliable integration, content based routing and global data delivery, and high volume monitoring and data ingestion.
• Spring: Takes the complexity out of Enterprise Java.
• vFabric: Provides a proven runtime platform for your Spring applications.
Disruptive Big Data products – MPP & Column store database, in memory data processing and Hadoop
• Pivotal Greenplum Database: A massively parallel platform for large-scale data analytics warehouse to manage, store and analyze petabytes of data.
• Pivotal GemFire: A real time distributed data store that with linear scalability and continuous uptime capabilities.
• Pivotal HD with HAWQ and GemFire XD: Commercially supported Apache Hadoop. HAWQ brings Enterprise class SQL capabilities and GemFireXD brings real time data access to Hadoop.
• Pivotal CF: Next generation enterprise PaaS – Pivotal CF makes applications the new unit of deployment and control (not VMs or middleware), radically improving developer productivity and operator agility.
Accelerate and help enterprise extract insights from their data assets. Pivotal analytic products offer advanced query and visualization capabilities to business analysts.
Q2. Who are your current customers and how do they typically use your products?
Susheel Kaushik: We have customers in all business verticals – Finance, Telco, Manufacturing, Energy, Medical, Retail to name a few.
Some of the typical uses of the products are:
• Big Data Store: Today, we find enterprises are NOT saving all of the data – cost efficiency is one of the reasons. Hadoop brings the price of the storage tier to a point where storing large amounts of data is not cost prohibitive. Enterprises now have mandates to not throw away any data in the hope that they can later unlock the potential insights from the data.
• Extend life of Existing EDW systems: Today most of the EDW system are challenged on the storage and processing aspects to provide a cost effective solution internally. Most of the data stored in EDW is not analyzed and the Pivotal Big Data products provide a platform for the customers to offload some of the data storage and the analytics processing. This offloaded processing, typically ETL like workloads, is ideal for the Big Data platforms. As a result, the processing times are reduced and the ETL relieved EDW now has excess capacity to satisfy the needs for some more years – thereby extending the life.
• Data Driven Applications: Some of the advanced enterprises already have peta-byte of varying formats data and are looking to derive insights from the data in real time/interactive time. These customers are building scalable applications leveraging the insights to assist business in decisioning (automated/manual).
In addition, Customers prefer the deployment choice provided from the Pivotal products, some prefer the bare metal infrastructure whereas some prefer the cloud deployment (on premise or the public clounds).
Q3. What are the main new technical features you are currently working on and why?
Susheel Kaushik: Here are some of the key technical features we are working on.
1. Better integration with HDFS
a. HDFS is becoming a cost effective storage interface for the enterprise customers. Pivotal is investing making the integration with HDFS even better. Enterprise customers demand security and performance from HDFS and we are actively investing in these capabilities.
In addition, storing the data in a single platform reduces the data duplication costs along with the data management costs to manage the multiple copies.
2. Integration with other Open Source projects
a. We are investing in Spring and Cloud foundry to integrate better with Hadoop. Spring and Cloud Foundry have a healthy eco system already. Making Hadoop easier to use for these users increases the talent pool available to build next generation data applications for Hadoop data.
3. SQL as a standard interface
a. SQL is the most expressive language for data analysis and enterprise customers have already made massive investments in training their workforce on SQL. We are investing heavily in bringing SQL as the standard interface for accessing in real time (GemFire XD) and interactive (HAWQ) response times enabling enterprises to leverage their existing workforce for Hadoop processing.
4. Improved Manageability and Operability
a. Managing and operating clusters is not easy for Hadoop and some of our enterprises do not have in-house capabilities to build/manage these large scale clusters. We are innovating to provide a simplified interface to manage and operate these clusters.
5. Improved Quality of Service
a. Resource contention is a challenge in any multi-tenant environment. We are actively working to make resource sharing in a multi-tenant environment easier. We already have products in the portfolio (MoreVRP) that allow customers to do fine grain control at CPU and IO level. We are making active investments to bring this capability across multiple processing paradigms.
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