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December 17, 2012

Apache Ambari: Hadoop Operations, Innovation, and Enterprise Readiness

Filed under: Apache Ambari,Hadoop,MapReduce — Patrick Durusau @ 4:23 pm

Apache Ambari: Hadoop Operations, Innovation, and Enterprise Readiness by Shaun Connolly

From the post:

Over the course of 2012, through Hortonworks’ leadership within the Apache Ambari community we have seen the rapid creation of an enterprise-class management platform required for enabling Apache Hadoop to be an enterprise viable data platform. Hortonworks engineers and the broader Ambari community have been working hard on their latest release, and we’d like to highlight the exciting progress that’s been made to Ambari, a 100% open and free solution that delivers the features required from an enterprise-class management platform for Apache Hadoop.

Why is the open source Ambari management platform important?

For Apache Hadoop to be an enterprise viable platform it not only needs the Data Services that sit atop core Hadoop (such as Pig, Hive, and HBase), but it also needs the Management Platform to be developed in an open and free manner. Ambari is a key operational component within the Hortonworks Data Platform (HDP), which helps make Hadoop deployments for our customers and partners easier and more manageable.

Stability and ease of management are two key requirements for enterprise adoption of Hadoop and Ambari delivers on both of these. Moreover, the rate at which this project is innovating is very exciting. In under a year, the community has accomplished what has taken years to complete for other solutions. As expected the “ship early and often” philosophy demonstrates innovation and helps encourage a vibrant and widespread following.

A reminder that tools can’t just be cool or clever.

Tools must fit within enterprise contexts where “those who lead from behind” are neither cool nor clever. But they do pay the bills and so are entitled to predictable and manageable outcomes.

Maybe. 😉 But that is the usual trade-off and if Apache Ambari helps Hadoop meet their requirements, so much the better for Hadoop.

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