The conclusion to this series covers Combiner-like aggregation functionality, counters, partitioning, and serialization.
Apache Spark is rising in popularity as an alternative to MapReduce, in a large part due to its expressive API for complex data processing. A few months ago, my colleague, Sean Owen wrote a post describing how to translate functionality from MapReduce into Spark, and in this post, I’ll extend that conversation to cover additional functionality.
We’re pleased to announce the release of Cloudera Enterprise 5.4 (comprising CDH 5.4, Cloudera Manager 5.4, and Cloudera Navigator 2.3).
Cloudera Enterprise 5.4 (Release Notes) reflects critical investments in a production-ready customer experience through governance, security, performance and deployment flexibility in cloud environments. It also includes support for a significant number of updated open standard components–including Apache Spark 1.3, Impala 2.2, and Apache HBase 1.0 (as well as unsupported beta releases of Hive-on-Spark data processing and OpenStack deployments).
Thanks to Chen Song, Data Team Lead at AppNexus, for allowing us to republish the following post about his company’s use case for Apache Parquet (incubating at this writing), the open standard for columnar storage across the Apache Hadoop ecosystem.
At AppNexus, over 2MM log events are ingested into our data pipeline every second. Log records are sent from upstream systems in the form of Protobuf messages. Raw logs are compressed in Snappy when stored on HDFS.
Thanks to Sam Shuster, Software Engineer at Edmunds.com, for the guest post below about his company’s use case for Spark Streaming, SparkOnHBase, and Morphlines.
Every year, the Super Bowl brings parties, food and hopefully a great game to appease everyone’s football appetites until the fall. With any event that brings in around 114 million viewers with larger numbers each year, Americans have also grown accustomed to commercials with production budgets on par with television shows and with entertainment value that tries to rival even the game itself.
Use the scripts and screenshots below to configure a Kerberized cluster in minutes.
Kerberos is the foundation of securing your Apache Hadoop cluster. With Kerberos enabled, user authentication is required. Once users are authenticated, you can use projects like Apache Sentry (incubating) for role-based access control via GRANT/REVOKE statements.
Taming the three-headed dog that guards the gates of Hades is challenging, so Cloudera has put significant effort into making this process easier in Hadoop-based enterprise data hubs.