Just over 6 months after launching Pivotal Big Data Suite (BDS), Pivotal customers and partners are rallying significant endorsements for how the new consumption model for Pivotal big data products is working for them. As one of the fastest growing new product offerings in the Pivotal portfolio, this is a ringing endorsement that the technology maturity as well as the consumption model is hitting the sweet spot for customers and partners alike. Check out what customers and partners have to say about using Big Data Suite so far.
The newest versions of SQLFire and GemFire XD are one and the same: Pivotal GemFire XD version 1.3. What were previously two separate products are now merged, so current licensees of either product are entitled to upgrade to the new version. Users of prior versions of GemFire XD will see a relatively small delta from the solution they are currently running, while SQLFire customers will find a significant improvement with some major new capabilities.
Over the past five years, we have seen the Hadoop ecosystem grow at an escalating pace. This week’s Strata and Hadoop World conference in New York is a testament to the level of interest this evolution has created among enterprises looking to expand their data analytic capabilities. So this is the right time to take a step back and think about what business problems we are trying to solve and how the various solutions in the market align with business objectives: the business problems and use-cases; cost and performance goals; as well as policy, maturity and regulatory needs. This post explores the trade-offs between these business objectives, and provides some guidance for how to navigate these technology decisions in today's modern big data landscape.
The latest release of Pivotal Greenplum Database, version 4.3.3, adds a number of notable updates, including Delta Compression. This exciting update adds an additional way to compress data in a column to save space. Internal tests and customer data have demonstrated well over 100x compression on 10G worth of TIME values from a dataset.
In this week's episode, Simon shares insights on why organisations want to use Platform-as-a-Service, and a more detailed tour of Pivotal CF and its components. This includes some of the high level ideas behind the platform, why it is built the way it is–and looking “under the covers” as to the major components that make up the platform.
In this post, Pivotal Cloud Foundry expert, Glyn Normington, explains how Cloud Foundry's container technology works. Named Garden, the application includes a generally platform agnositc front end and a platform-specific backend. The Linux backend relies on standard Linux containers and operating system features such as namespaces, control groups, and various resource control and networking facilities to isolate containers from each other and limit their impact on the host virtual machine. Garden is designed to create containers and provide telemetry, managing the container life cycle.
The Future Architecture of a Data Lake: In-memory Data Exchange Platform Using Tachyon and Apache Spark
Pivotal is revolutionizing the data lake with an architecture that builds upon disk-based storage with memory-centric processing frameworks. In partnership with the AMPLab at UC Berkeley, Pivotal envisions this future architecture will incorporate an in-memory data exchange platform based on Tachyon and in-memory compute layer augmented by Apache Spark. This post outlines how Tachyon works in a "butterfly architecture" to revise the Data Lake architecture, and also invites you to learn more about Tachyon in person at Pivotal's New York meetup, “Evolution of Data Architectures: Pivotal’s Data Lake Vision for 2015,” on Wednesday October 15th at 7pm at the Pivotal Labs New York Office, located on 625 Avenue of Americas, 2nd Floor, New York, NY. For those unable to attend, the talk will be recorded and available online following the event.
Pivotal data science and security expert, Derek Lin, has considerable experience in the areas of big data, software development, and analysis for security, risk, fraud, and online banking. In this post, he explains how Pivotal data science teams are providing thought leadership and clear paths to solving security analytics problems. Lin provides background and then gives a real-world approach for associating and discovering behaviors across help desk tickets and command-line activities, ultimately preventing misuse from those with privileged access.