Thanks to new improvements in Hue, CDH 5.2 offers the best GUI yet for using Hadoop.
CDH 5.2 includes important new usability functionality via Hue, the open source GUI that makes Apache Hadoop easy to use. In addition to shipping a brand-new app for managing security permissions, this release is particularly feature-packed, and is becoming a great complement to BI tools from Cloudera partners like Tableau, MicroStrategy, and Zoomdata because a more usable Hadoop translates into better BI overall across your organization!
Impala authentication can now be handled by a combination of LDAP and Kerberos. Here’s why, and how.
Impala, the open source analytic database for Apache Hadoop, supports authentication—the act of proving you are who you say you are—using both Kerberos and LDAP. Kerberos has been supported since release 1.0, LDAP support was added more recently, and with CDH 5.2, you can use both at the same time.
Using LDAP and Kerberos together provides significant value;
This new feature, jointly developed by Cloudera and Intel engineers, makes management of role-based security much easier in Apache Hive, Impala, and Hue.
Apache Sentry (incubating) provides centralized authorization for services and applications in the Apache Hadoop ecosystem, allowing administrators to set up granular, role-based protection on resources, and to review them in one place. Previously, Sentry only designated administrators to GRANT and REVOKE privileges on an authorizable object.
Impala 2.0 is the most SQL-complete/SQL-compatible release yet.
As we reported in the most recent roadmap update (“What’s Next for Impala: Focus on Advanced SQL Functionality”), more complete SQL functionality (and better SQL compatibility with other vendor extensions) is a major theme in Impala 2.0.
In this post, we’ll describe the highlights (not a complete list), and provide links to the docs that drill-down on these functions.
Cloudera Labs contains ecosystem innovations that one day may bring developers more functionality or productivity in CDH.
Since its inception, one of the defining characteristics of Apache Hadoop has been its ability to evolve/reinvent and thrive at the same time. For example, two years ago, nobody could have predicted that the formative MapReduce engine, one of the cornerstones of “original” Hadoop, would be marginalized or even replaced. Yet today, that appears to be happening via Apache Spark,