The recently-released Apache Hive 2.0 contains some exciting improvements, many of which are already available in CDH 5.x.
Recently, the Apache Hive community announced Hive 2.0.0. This is a larger release compared to the previous one (covered here), with a lengthy list of new features (many experimental), enhancements, and bug fixes. Cloudera’s Hive team have been working with the community for months to drive toward this significant release.
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.
Fixes in CDH 5.5 make writing Parquet data for Apache Impala (incubating) much easier.
Over the last few months, several Cloudera customers have provided the feedback that Parquet is too hard to configure, with the main problem being finding the right layout for great performance in Impala. For that reasons, CDH 5.5 contains new features that make those configuration problems go away.
Auto-Detection of HDFS Block Size
Cloudera Enterprise 5.5 improves the life of the admin through a deeper integration between HUE and Cloudera Manager, as well as a rebase on HUE 3.9.
Cloudera Enterprise 5.5 contains a number of improvements related to HUE (the open source GUI that makes Apache Hadoop easier to use), including easier setup for HUE HA, built-in activity monitoring for improved stability, and better security and reporting via Cloudera Navigator and Apache Sentry (incubating).
Recent improvements to Apache Hadoop’s native backup utility, which are now shipping in CDH, make that process much faster.
DistCp is a popular tool in Apache Hadoop for periodically backing up data across and within clusters. (Each run of DistCp in the backup process is referred to as a backup cycle.) Its popularity has grown in popularity despite relatively slow performance.
In this post, we’ll provide a quick introduction to DistCp.