{"id":3131,"date":"2014-02-06T19:13:33","date_gmt":"2014-02-06T19:13:33","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=2884"},"modified":"2014-02-06T19:13:33","modified_gmt":"2014-02-06T19:13:33","slug":"big-data-three-questions-to-voltdb","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2014\/02\/big-data-three-questions-to-voltdb\/","title":{"rendered":"Big Data: Three questions to VoltDB."},"content":{"rendered":"

“Some of our current priorities include: augmenting capabilities in the area of real-time analytics \u2013 especially around online operations, SQL functionality, integrations with messaging applications, statistics and monitoring procedures, and enhanced developer features.”– Ryan Betts.<\/strong><\/p><\/blockquote>\n

The third interview in the \u201cBig Data: three questions to <\/em>\u201c series of interviews, is with Ryan Betts<\/strong>, CTO of VoltDB<\/a>.<\/p>\n

RVZ<\/p>\n

Q1. What are your current product offerings? <\/strong><\/p>\n

Ryan Betts<\/strong>: VoltDB<\/a> is a high-velocity database platform that enables developers to build next generation real-time operational applications. VoltDB converges all of the following:<\/p>\n

\u2022\tA dynamically scalable in-memory relational database delivering high-velocity, ACID-compliant OLTP
\n\u2022\tHigh-velocity data ingestion, with millions of writes per second
\n\u2022\tReal-time analytics, to enable instant operational visibility at the individual event level
\n\u2022\tReal-time decisioning, to enable applications to act on data when it is most valuable\u2014the moment it arrives <\/p>\n

Version 4.0 delivers enhanced in-memory analytics capabilities and expanded integrations. VoltDB 4.0 is the only high performance operational database that combines in-memory analytics with real-time transactional decision-making in a single system.
\nIt gives organizations an unprecedented ability to extract actionable intelligence about customer and market behavior, website interactions, service performance and much more by performing real-time analytics on data moving at breakneck speed.<\/p>\n

Specifically, VoltDB 4.0 features a tenfold throughput improvement of analytic queries and is capable of writes and reads on millions of data events per second. It provides large-scale concurrent, multiuser access to data, the ability to factor current incoming data into analytics, and enhanced SQL support. VoltDB 4.0 also delivers expanded integrations with an organization’s existing data infrastructure such as message queue systems, improved JDBC driver and monitoring utilities such as New Relic.<\/p>\n

Q2. Who are your current customers and how do they typically use your products? <\/strong><\/p>\n

Ryan Betts<\/strong>: Customers use VoltDB for a wide variety of data-management functions, including data caching, stream processing and “on the fly” ETL<\/a>.
\nCurrent VoltDB customers represent industries ranging from telecommunications to e-commerce, power & energy, financial services, online gaming, retail and more.<\/p>\n

Following are common use cases:<\/p>\n

\u2022\tOptimized, real-time information delivery
\n\u2022\tPersonalized audience targeting
\n\u2022\tReal-time analytics dashboards
\n\u2022\tCaching server replacements
\n\u2022\tSession \/ user management
\n\u2022\tNetwork analysis & monitoring
\n\u2022\tIngestion and on-the-fly-ETL<\/p>\n

Below are the customers that have been publicly announced thus far: <\/p>\n

Eagle Investments<\/a>
\nConexient<\/a>
\nOpenNet<\/a>
\nSakura<\/a>
\nShopzilla<\/a>
\nSocial Game Universe<\/a>
\nYellowhammer<\/a><\/p>\n

Q3. What are the main new technical features you are currently working on and why?<\/strong><\/p>\n

Ryan Betts<\/strong>: Our customers are reaping the benefits of VoltDB in the areas of transactional decision-making and generating real-time analytics on that data\u2014right at the moment it\u2019s coming in. <\/p>\n

Therefore, some of our current priorities include: augmenting capabilities in the area of real-time analytics \u2013 especially around online operations, SQL functionality, integrations with messaging applications, statistics and monitoring procedures, and enhanced developer features. <\/p>\n

Although VoltDB has proven to be the industry\u2019s \u201ceasiest to use\u201d database, we are also continuing to invest quite heavily in making the process of building and deploying real-time operational applications with VoltDB even easier. Among other things, we are extending the power and simplicity that we offer developers in building high throughput applications to building modest sized throughput applications. <\/p>\n

—————
\nRelated Posts<\/strong><\/p>\n

– Setting up a Big Data project. Interview with Cynthia M. Saracco.
\nODBMS Industry Watch, January 27, 2014<\/a><\/p>\n

– Big Data: Three questions to Pivotal.
\nODBMS Industry Watch, January 20, 2014.<\/a><\/p>\n

–Big Data: Three questions to InterSystems.
\nODBMS Industry Watch, January 13, 2014.<\/a> <\/p>\n

– Operational Database Management Systems. Interview with Nick Heudecker.
\nODBMS Industry Watch, December 16, 2013.<\/a> <\/p>\n

Resources<\/strong><\/p>\n

ODBMS.org: Free resources on Big Data, Analytics, Cloud Data Stores, Graphs Databases, NewSQL, NoSQL, Object Databases. <\/a><\/p>\n

  • Follow ODBMS.org on Twitter: @odbmsorg<\/a><\/li>\n

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    “Some of our current priorities include: augmenting capabilities in the area of real-time analytics \u2013 especially around online operations, SQL functionality, integrations with messaging applications, statistics and monitoring procedures, and enhanced developer features.”– Ryan Betts. The third interview in the \u201cBig Data: three questions to \u201c series of interviews, is with Ryan Betts, CTO of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[35,66,650,616],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/3131"}],"collection":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/comments?post=3131"}],"version-history":[{"count":0,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/3131\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=3131"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=3131"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=3131"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}