We'll respond shortly.
If you want to spin up a Apache Hadoop cluster, you need to grapple with the question of how to attach your disks. Historically, this decision has favored direct attached storage (DAS). However, technology has advanced and shared storage costs have declined, making network attached storage (NAS) a cleaner approach for many. This article lays out the cost-benefits in today’s technology landscape, including relevant performance data to help you decide if NAS or DAS is right for your Hadoop deployment.
Starting today, Pivotal Cloud Foundry will allow developers and operations teams a near real-time view into the health and performance of their applications through PCF Metrics. The new capabilities are designed to give developers fast feedback and allow them to quickly take action. This post highlights the functionality, shares screenshots, and explains what it can do for your Pivotal Cloud Foundry environments.
For fine tuning Pivotal HAWQ on EMC Isilon’s HDFS, there are a number of configuration and performance parameters to consider, and this post explains all of the key ones. Pivotal’s Platform Engineering team has run all 99 TPC-DS tests successfully under different configurations to offer guidance and help customers optimize the performance of their environments
In part one of this five blog series, Dan Baskette, Pivotal Director of Marketing for Data and Analytics, quickly explains the impact of SQL performance on Hadoop and tees up the overall findings of an unaudited TPC-DS performance test, comparing Pivotal HAWQ, Apache Hive™, and Impala. He begins by explaining how speed, quality, completeness, and query effectiveness are important considerations in the world of SQL on Hadoop. Then, he outlines overall test results, testing environments, and requirements.
A technical white paper recently published by GE Power & Water and GE Global Research highlights the benefits of running Pivotal products for storing large amounts of data, while simultaneously taking in new data and deploying real-time analysis. The paper focuses on a series of performance, stability and load tests that GE did on Pivotal GemFire. The results of the tests were impressive, with the final round running for 5 days, ingesting 100K points per second and storing three days’ worth of replicated high-velocity data in memory across a large cluster of 46 machines.
With India’s massive online population growth, the e-ticketing systems supporting India Rail faced a major challenge with scale. At a recent forum in India, the team responsible for the applications explained how they overcame the scale and other issues with Pivotal GemFire, tripling application throughput.
Sometimes you come across a problem where your go-to approaches start to fail you. I came across one of these examples recently where a Rails app required a report to be built which consumed data from almost every model within … Read more
Pivotal’s Analytics Workbench offers partners the power of a 1000-node Hadoop cluster, providing a publicly-available data processing powerhouse, free of charge, for 90 day engagements. It also offers a powerful platform for testing. To this end, the AWB team collaborated in October with Pivotal’s partner Mellanox Technologies to test the effect of flipping the cluster’s network connection between InfiniBand and Ethernet on-the-fly, affording customers unparalleled flexibility for cost savings in their Hadoop deployments. This approach also proved they were able to run the cluster in mixed mode. Mixed mode allows you to use one vendor and bridge separate network-connected platforms—for example, connecting a Hadoop cluster to both EMC storage arrays and VMware vSphere. Such an approach reduces cost, difficulty, and latency.