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Big Data: three questions to DataStax

by Roberto V. Zicari on April 7, 2014

“High volume and data driven businesses have led to new types of data emerging from the cloud, mobile devices, social media and sensor devices. For applications processing such data, traditional relational databases such as Oracle simply run out of steam.”–Robin Schumacher

The sixth interview in the “Big Data: three questions to “ series of interviews, is with Robin Schumacher, VP of Products at DataStax.


Q1. What is your current product offering?

Robin Schumacher: DataStax offers the first enterprise-class NoSQL platform for data-driven, real-time online applications. Our flagship product is DataStax Enterprise 4.0, built on Apache Cassandra. It is a complete big data platform with the full power of Cassandra offering a range of solutions including built in analytics, integrated search, an in-memory options, and the most comprehensive security feature set of any NoSQL database.
An integrated analytics component allows users to store and manage line of business application data and analyzes that same data within the platform. The analytics capability allows for comprehensive workload management and allows the user to run real time transactions and enterprise search workloads in a seamlessly integrated database.
Built in search offers robust full text search, faceted search, rich document handling and geospatial search.
Benefits include full workload management, continuous availability, real-time functionality and data protection.
Lastly, security runs through the entire platform to protect unauthorized access to guard sensitive data. Visual backup and restore processes make for retrieving lost data extremely easy.
DataStax OpsCenter, a simplified management solution, is included with DataStax Enterprise. This service makes it easy to manage Cassandra and DataStax Enterprise clusters by giving administrators, architects and developers a view of the system from a centralized dashboard. OpsCenter installs seamlessly and gives system operators the flexibility to monitor and manage the most complex workloads from any web browser.

Q2. Who are your current customers and how do they typically use your products?

Robin Schumacher: DataStax is the first viable alternative to Oracle and powers the online applications for 400+ customers and more than 20 of the Fortune 100. Our customer industries range from e-commerce to education to digital entertainment and the top use cases are the following:
1. Fraud detection
2. The Internet of Things
3. Messaging
4. Personalization
5. Collections/Playlists

Customers include Netflix, eBay, Adobe, Amara Health Analytics and many others.

The most common baseline use for our product is to serve as an operational database management system for online applications that must scale to incredible levels and must remain online at all times.

Q3. What are the main new technical features you are currently working on and why?

Robin Schumacher: We recently added an in-memory option that enables companies to process data up to 100 times faster. This option excels in use cases that require fast write and read operations, and is particularly suited when data is overwritten frequently, but not actually deleted. DataStax Enterprise 4.0 is the first NoSQL database to combine this in memory option with Cassandra¹s always on architecture, linear scalability and datacenter support, delivering lightning performance that allows businesses to scale applications with zero downtime – particularly useful in financial services use cases or any application where performance is key.

High volume and data driven businesses have led to new types of data emerging from the cloud, mobile devices, social media and sensor devices. For applications processing such data, traditional relational databases such as Oracle simply run out of steam. DataStax Enterprise 4.0 offers a powerful, modern alternative to help build online applications that scale as the business grows. This in-memory capability equals faster performance, easy development, flexible performance management and seamless search:
Objects created in-memory optimize performance and deliver increased speed which enables businesses to deliver data to customers faster than ever before.
In-memory objects act as Cassandra tables so they are transparent to applications and developers have no learning curve to manage.Administrators can decide where to assign data, making performance optimization easier than ever.

Enhanced internal cluster communications deliver faster search operations help developers build applications more efficiently.

Related Posts

Big Data: Three questions to Aerospike. ODBMS Industry Watch, March 2, 2014

– Big Data: Three questions to McObject. ODBMS Industry Watch, February 14, 2014

– Big Data: Three questions to VoltDB. ODBMS Industry Watch, February 6, 2014.

– Big Data: Three questions to Pivotal. ODBMS Industry Watch, January 20, 2014.

Big Data: Three questions to InterSystems. ODBMS Industry Watch, January 13, 2014.

– Operational Database Management Systems. Interview with Nick Heudecker, ODBMS Industry Watch, December 16, 2013.

Resources free download of technical resources on DataStax free download of technical resources on Apache Cassandra

2013 Gartner Magic Quadrant for Operational Database Management Systems by Donald Feinberg, Merv Adrian, Nick Heudecker, October 21, 2013

Follow on Twitter@odbmsorg

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