Cluster admins will love the new cluster utilization reporting available in Cloudera Manager 5.7.
Enterprise data hub clusters often are shared by several teams. In such multi-tenant environments, cluster administrators are required to ensure that resources are shared fairly so that one tenant cannot run jobs that starve others. To give better visibility into resource consumption in multi-tenant environments, Cloudera Manager 5.7 (in Cloudera Enterprise Flex and Data Hub Editions) has a new feature for reporting cluster utilization that provides information about overall cluster usage,
Cloudera has given its documentation set a facelift, and we think you’ll like the new look. We use more whitespace and a font that is easier to read and skim, and your pages load much faster. But the improvements go beyond the merely aesthetic.
While electronic documentation has been around for decades, most online documentation is still presented as if it were printed in books. There is a table of contents that assumes you will read the content from start to finish.
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.
The Cloudera Developer Program is kind of amazing. Here’s why.
For those with a desire to build new applications on Cloudera’s platform, historically there’s been a gap to cross between pure bootstrapping on CDH (whether via a small on-premise cluster, in the public cloud, or using Cloudera Live) and obtaining full-blown support for a complete enterprise data hub with all the fixings (including Cloudera Navigator). For individuals who have moved beyond self-learning and are getting “serious,”
Enabling Python development on CDH clusters (for PySpark, for example) is now much easier thanks to new integration with Continuum Analytics’ Python platform (Anaconda).
Python has become an increasingly popular tool for data analysis, including data processing, feature engineering, machine learning, and visualization. Data scientists and data engineers enjoy Python’s rich numerical and analytical libraries—such as NumPy, pandas, and scikit-learn—and have long wanted to apply them to large datasets stored in Apache Hadoop clusters.