Cloudera Enterprise 5.11 is Now Available
Cloudera is pleased to announce that Cloudera Enterprise 5.11 is now generally available (GA). The highlights of this release include lineage support for Apache Spark, Apache Kudu security integration, embedded data discovery for self-service BI, and new cloud capabilities for Microsoft ADLS and Amazon S3.
As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack. Here is a partial list of what’s included (see the Release Notes for a full list):
- Core Platform and Cloud
- Amazon S3 Consistency: S3Guard ensures that operations on Amazon S3 are immediately visible to other clients,
Cloudera Director helps you deploy, scale, and manage Apache Hadoop clusters in the cloud of your choice. Its enterprise-grade features deliver a reliable mechanism for establishing production-ready clusters in the cloud for big-data workloads and applications in a simple, reliable, automated fashion.
Cloudera Director Overview
In this post, you will learn about new functionality in release 2.3, but first, if you’re new to Cloudera Director, let’s revisit what it does.
- On-demand creation and termination of clusters: Using Cloudera Director,
Cloudera is proud to announce that Cloudera Enterprise 5.10 is now generally available (GA). The highlights of this release include the GA of the new columnar storage engine Apache Kudu, improved cloud performance and cost-optimizations, and cloud-native data governance for Amazon S3.
As usual, there are also a number of quality enhancements and bug fixes (learn more about our multi-dimensional hardening/QA process) and other improvements across the stack. Here is a partial list of what’s included (see the Release Notes for a full list):
Since the launch of sparklyr, working with Apache Spark in Apache Hadoop has become much easier for R users. sparklyr contains a dplyr interface into Spark and allows users to leverage crucial machine learning algorithms from Spark MLlib and H2O Sparkling Water. This greatly reduces the barrier of entry for R users in adopting Spark as a tool for big data and should go a long way in enabling R workloads to migrate to Hadoop.
Apache Impala (incubating) includes several features that allow you to restrict or allocate resources so as to maximize stability and performance for your Impala workloads. You can limit both CPU and memory resources used by Impala to manage and prioritize jobs on CDH clusters. This blog post describes the techniques a typical Impala deployment can use to manage its resources.
Static Service Pools
Static service pools isolate services from one another, so that a high load on one service has limited impact on other services.