Where can we expect to see Pivotal at the JavaOne and Open World conferences this year? Pivotal is exhibiting at JavaOne booth 5201, and presenting at 11 different sessions. Which talks are you looking forward to most? Pivotal technologists are presenting a lot of sessions, particularly related to open source contributions in Spring, Groovy, cloud applications, and developing with microservices. The talks include John Field’s ‘The Anatomy of a Secure Web Application Using Java [CON3479]’, Guillaume Laforge’s ‘Groovy in 2014 and Beyond [CON5996]’, Cornelia Davis’ ‘Running Your Spring Apps in the Cloud [CON4327]’ and Josh Long’s Spring 4TW! [CON3693]. Although not presented by Pivotal technologists, we will definitely be checking out Groovy and Grails Puzzlers: As Usual—Traps, Pitfalls, and End Cases[CON1764]. Can you tell us a little bit about the latest changes you've made to the Pivotal Big Data Suite? Last Tuesday, Pivotal announced the release of version 8.0 of Pivotal GemFire - the distributed, in-memory NoSQL database that is part of Pivotal Big Data Suite. Top line new capabilities include; RESTful API so that applications in additional programming languages can now use GemFire 8 to increase their scale and performance: Scala, Node.JS, Python, Ruby. Improved scale with in-memory compression allowing 50% more data per node. Improved availability and resilience: rolling upgrades mean clusters can stay up and running even when their software is being upgraded, eliminating any need for planned downtime of a distributed application; significant automation on node restart means that clusters are now self-healing without need for intervention from administrators to be reconstructed after an Internet or system failure. Also relevant to Java One; we've upgraded support for the Spring Data GemFire API. We have also revealed a new customer, India Railways, which uses Pivotal GemFire to power its highly scaled ticketing application, joining China National Railways who has a similar application serving the entire nation of China for its railway ticket purchasing. Can you comment on any other projects and areas that Pivotal is currently working on? We continue to enhance all components of Pivotal Big Data Suite that serve use cases for real-time structured data, massively-parallel processing of data for advanced analytics use cases, all of which center around Pivotal's Hadoop distribution. We have just released a new version of GemFire XD, our in-memory distributed database for SQL-based data, which supports high concurrency-scale out applications with structured data, and allows for persistence and archival either with traditional storage, or with Pivotal HD. Similarly we've recently released a new version of Pivotal HD, bringing us in compliance with Apache Hadoop 2.2. We also just announced release 1.3 of Pivotal CF, the leading enterprise PaaS powered by Cloud Foundry. Gartner recently downgraded Big Data into the 'trough of disillusionment'. Do you also feel the hype around Big Data dwindling at the OpenWorld? “Big Data” is a big buzzword covering many use cases. We have customers realizing huge benefits from big data implementations based on Pivotal’s Big Data Suite. These include General Electric and their implementation of a Data Lake to optimize aviation maintenance and China Railways which leverages the GemFire in-memory database in Pivotal Big Data Suite to power their fast data requirements for scaling up their nation-wide ticket sales cloud application. Certainly, some aspects of big data have been overhyped, and customer expectations are coming closer to reality. For example, many enterprises are realizing that utilizing map reduce for big data analytics projects is problematic: 1) its slow, and 2) it's hard to find experienced developers to develop these queries when, in fact, most enterprises already have a lot of staff that are experienced with SQL, but cannot take advantage of many of these newer platforms. This is why Pivotal chose to innovate with SQL on Hadoop solutions for analytics with our massively parallel processing (MPP) HAWQ query engine - this is the only SQL engine in the market that runs natively in Hadoop nodes, and is able to complete the TCP-DS benchmark. We also have the highest scaling, in-memory solution for creating transactional SQL applications that persist operational and historical data into Hadoop: Pivotal GemFire XD. This is also why Pivotal is helping contribute to big data solutions in ways that help our customers realize value and enterprise class stability sooner, such as when we announced our support and contribution to the Apache Ambari project for Hadoop. Others aspects of big data are still very early days, yet Pivotal believes there is a lot of opportunity here. Applying in-memory technologies for big data batch processing, analytics, and real-time applications is one such example. Pivotal already has mature, marketing-leading technology in productive use serving extreme applications in industry segments such as financial trading applications, and real-time enterprise risk assessments. We are well positioned to serve general enterprise use cases for real-time big data applications as their needs catch up to the extreme application requirements that Pivotal has been serving for some time.