Ed. Note: This blog post was authored by Brandon Draeger, Director of Marketing and Strategy, Intel Big Data Solutions, Data Center Group
Over time and with the advent of new capabilities, Apache Hadoop use cases have evolved to include near real-time processing. Just as Dell, Cloudera, and Intel collaborated on Hadoop ETL offload and other batch processing scenarios, we’re now pushing the edge with streaming analytics usage models by building distributed systems using innovative software components such as Apache Spark (incubating) that continue to come to maturity within the Apache Hadoop ecosystem.
As we saw with the early adoption of Apache Hadoop, operational barriers to entry often existed for enterprises due to a mismatch in supply versus demand for big data administrator skill-sets. In 2011, Dell, Cloudera, and Intel addressed this challenge by teaming up to build validated reference architectures on Intel Xeon processor-based Dell PowerEdge servers running the Cloudera Distribution for Apache Hadoop. In doing so, we took the guesswork out of initial deployments and enabled enterprises to begin their big data journey with confidence on a fully supported solution.
Following in that same tradition, these three industry leaders are again joining together to address the new adoption challenges of in-memory analytics by bringing to market the Dell In-Memory Appliances for Cloudera Enterprise for both mid-market and enterprise customers. For the mid and enterprise markets respectively, Intel Xeon processor E5-2600 v2 based PowerEdge R720XD and Intel Xeon processor E7-4800 v2 based PowerEdge R920 will serve as the optimized building blocks for running Cloudera Enterprise Data Hub Edition, including Apache Spark.
Soon, near real-time analysis of massive data sets will be a commonplace capability for businesses. Via the planned Dell In-Memory Appliances for Cloudera Enterprise, Dell, Cloudera, and Intel will once again provide a practical entry point for customers to begin evaluating these transformative capabilities and embark on their path to in-memory analytics adoption.
