Our Customers at Pivotal Recognize the Importance of Bridging Traditional Data Warehousing into Next Generation Platform
Recently Gartner published the report, “Gartner Critical Capabilities for Data Warehouse Database Management Systems” that shares survey results of customers from a variety of Data Warehouse solution vendors. Based on existing customer implementations and their experiences with data warehouse DBMS products, the report scored Pivotal in the top 2 out of 16 vendors in two use cases: “Traditional Data Warehouse” and “Logical Data Warehouse”. In a third use case, “Context Independent Data Warehouse”, Pivotal scored in the top 3 relative to the 15 other vendors. These results show that Pivotal customers hold their Greenplum Database in high regard for traditional data warehousing approaches, as well as emerging data warehousing use cases. This post covers the kinds of insights these customers are seeking, how quickly they are getting there with modern approaches, and how Pivotal is helping them do even more with data.
Last week we announced an important new partnership with Cloudbees to bring a native enterprise Jenkins service to Pivotal CF (PCF). This provided a first glimpse at the power of Pivotal Network, a service catalog for PCF, that allows any private PCF installation to import additional scalable platform services from Pivotal and a growing ecosystem of partners. In the wake of this milestone, Pivotal's James Watters takes this opportunity to articulate how this is yet another positive signal for the rapidly growing enterprise PaaS market, and how Cloud Foundry is serving up "fresh hope to IT people searching desperately for a private cloud strategy".
Today we’re announcing our partnership with CloudBees, the company behind Jenkins, to offer Pivotal customers access to the leading continuous integration and delivery software for enterprises. The CloudBees Enterprise Jenkins (CBJE) software will be delivered as an add-on service for Pivotal CF. Our customers will be able to download CBJE directly from Pivotal Network to install and configure Jenkins using Pivotal CF Operations Manager.
Award-winning Berkley astronomer, Onsi Fakhouri, has brought years of experience from working with Pivotal Labs and is currently leading the re-imagination and re-engineering of elastic runtime architectures on Cloud Foundry. This post covers Fakhouri’s background, Go (open source programming language) refactoring, Cloud Foundry Dojos, challenges of distributed systems, and more.
The SpringOne 2GX 2014 conference starts this evening in Dallas, TX, with nearly 1000 developers, architects, and operations experts expected to attend. Sponsored by Pivotal, SpringOne 2GX 2014 focuses on open source technologies such as Spring IO, Groovy & Grails, Cloud Foundry, Hadoop, and Tomcat.
This detailed post explores the complicated topic of how Pivotal Greenplum Database (GPDB) ensures all available CPU resources are used for processing a query in an analytic data warehouse environment. The post shares code used to run some tests and gather system statistics that illustrate how CPU usage in massively parallel databases and distributed computing works. In this post, we dive into the details in order to help users to understand the measurements, including breaking down each query into results across the distributed environment, in order to comprehend the details of how GPDB works.
Today's guest post is from Tom Wilson, President and Lead Technologist for Jack Russell Software, where he is responsible for the Software Development Vision and part of the Software Architecture Team. In this post, Tom explains why his team decided to break out new business logic for their parent company's (CareKinesis, Inc) flagship Rails application into a set of microservices running on Pivotal Web Services. A period of rapid growth made them feel the pain point of a monolithic Rails codebase. By moving to a micro-services stack on Pivotal Web Services, Tom and his team were able to add developers to build new features in days vs weeks and take advantage of lightweight containers for performance. Pivotal Web Services $0.03/GB-Hr pricing and fine-grained container sizing allowed them to do more for a lot less.
The number of images and videos captured by humans using digital cameras is staggering. It has been estimated that an average of 350 million photos are uploaded to Facebook daily, and about 100 hours of video are uploaded to Youtube every minute. This has resulted in enormous “image and video lakes,” a term we use to describe these ever-growing collections of images and videos. Such gigantic image lakes are not unique to consumer services —they are also found in domains such as healthcare and astronomy. In this post, we will demonstrate how a Content-Based Image Retrieval system can be easily and efficiently realized using Pivotal’s Hadoop Distribution (Pivotal HD) with HAWQ, our SQL engine for Hadoop.