The Apache Hadoop project announced the release of 3.0.0-alpha2 on January 25th, 2017. This is the second alpha release in the 3.0.0 release series leading up to 3.0.0 GA, and incorporates 857 new fixes, improvements, and features since 3.0.0-alpha1 last September. It’s worth reading our previous blog post about 3.0.0-alpha1; in this post, we’ll discuss the new improvements that landed in alpha2.
Classpath Isolation for Hadoop Client Jars
The pain of classpath isolation has been experienced by many Java developers.
Previously in Part 4, we described the most commonly used FairScheduler properties in Apache Hadoop. In Part 5, we’ll provide some examples to show how properties can be used, individually and in combination, to achieve commonly desired behavior such as application prioritization and organizing queues.
Example: Best Effort Queue
Summary: Create a “best effort” queue that runs applications when the cluster is underutilized.
Implementation: In FairScheduler,
Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.
This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,
Cloudera Enterprise 5.10 includes the latest updates of Hue, the intelligent editor for SQL Developers and Analysts.
As part of Cloudera’s continuing investments in user experience and productivity, Cloudera Enterprise 5.10 includes an updated version of Hue. We provide a summary of the main enhancements in the following part of this blog post. (Hue from C5.10 is also available for a quick try in one click on demo.gethue.com.)
The Hue editor keeps getting better with these major improvements:
The number of rows returned is displayed so you can quickly see the size of the dataset.
There are two clear trends in the big-data ecosystem: the growth of machine learning use cases that leverage large distributed data sets, and the growth of Spark’s Machine Learning libraries (often referred to as MLlib) for these use cases. In fact, Spark’s MLlib library is arguably the leading solution for machine learning on large distributed data sets.
Intel and Cloudera have collaborated to speed up Spark’s ML algorithms, via integration with Intel’s Math Kernel Library (Intel® MKL).