As part of the drumbeat for Spark Summit West in San Francisco (June 6-8), learn how analyzing stats from professional sports leagues is an instructive use case for data analytics using Apache Spark with SQL.
In the United States, many diehard sports fans morph into amateur statisticians to get an edge over the competition in their fantasy sports leagues. Depending on one’s technical chops, this “edge” is usually no more sophisticated than simple spreadsheet analysis,
Thanks to Richard Williamson of Silicon Valley Data Science for allowing us to republish the following post about his sample application based on Apache Spark, Apache Kudu (incubating), and Apache Impala (incubating).
Why should your infrastructure maintain a linear growth pattern when your business scales up and down during the day based on natural human cycles? There is an obvious need to maintain a steady baseline infrastructure to keep the lights on for your business,
In this guest post, members of the Barclays Advanced Data Analytics Team describe the results of an offsite hackathon to develop a recommendation system using Apache Spark.
In the depths of the cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a week to collaboratively solve a key business problem: how to design a better customer experience. We framed the problem in the context of using customer shopping behavior data to build a personalized recommender system.
Cloudera Enterprise 5.7 is now generally available (comprising CDH 5.7, Cloudera Manager 5.7, and Cloudera Navigator 2.6).
Cloudera is excited to announce the general availability of Cloudera Enterprise 5.7! Main highlights of this release include production-ready Hive-on-Spark functionality, which will help users accelerate their use of Apache Spark as a data processing standard; 4x performance gains for Apache Impala (incubating); easier cluster configuration and utilization reporting; and end-to-end encryption for Apache Spark data.
Users of the latest release of the Genome Analysis Toolkit, an open source framework for analyzing high-throughput DNA sequencing data, can now choose Apache Spark for data processing.
Ever since the Human Genome Project produced the first draft sequence of the human genome in 2000, the cost of sequencing has dropped exponentially, from around US$100 million per genome then to around US$1,000 today. Over the same period, we have seen massive growth in the storage and processing capabilities of big data technologies like Apache Hadoop.