Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

April 2, 2014

Hortonworks Data Platform 2.1

Filed under: Apache Ambari,Falcon,Hadoop,Hadoop YARN,Hive,Hortonworks,Knox Gateway,Solr,Storm,Tez — Patrick Durusau @ 2:49 pm

Hortonworks Data Platform 2.1 by Jim Walker.

From the post:

The pace of innovation within the Apache Hadoop community is truly remarkable, enabling us to announce the availability of Hortonworks Data Platform 2.1, incorporating the very latest innovations from the Hadoop community in an integrated, tested, and completely open enterprise data platform.

A VM available now, full releases to follow later in April.

Just grabbing the headings from Jim’s post:

The Stinger Initiative: Apache Hive, Tez and YARN for Interactive Query

Data Governance with Apache Falcon

Security with Apache Knox

Stream Processing with Apache Storm

Searching Hadoop Data with Apache Solr

Advanced Operations with Apache Ambari

See Jim’s post for some of the details and the VM for others.

January 18, 2013

Hortonworks Data Platform 1.2 Available Now!

Filed under: Apache Ambari,Hadoop,HBase,Hortonworks,MapReduce — Patrick Durusau @ 7:18 pm

Hortonworks Data Platform 1.2 Available Now! by Kim Rose.

From the post:

Hortonworks Data Platform (HDP) 1.2, the industry’s only complete 100-percent open source platform powered by Apache Hadoop is available today. The enterprise-grade Hortonworks Data Platform includes the latest version of Apache Ambari for comprehensive management, monitoring and provisioning of Apache Hadoop clusters. By also introducing additional new capabilities for improving security and ease of use, HDP delivers an enterprise-class distribution of Apache Hadoop that is endorsed and adopted by some of the largest vendors in the IT ecosystem.

Hortonworks continues to drive innovation through a range of Hadoop-related projects, packaging the most enterprise-ready components, such as Ambari, into the Hortonworks Data Platform. Powered by an Apache open source community, Ambari represents the forefront of innovation in Apache Hadoop management. Built on Apache Hadoop 1.0, the most stable and reliable code available today, HDP 1.2 improves the ease of enterprise adoption for Apache Hadoop with comprehensive management and monitoring, enhanced connectivity to high-performance drivers, and increased enterprise-readiness of Apache HBase, Apache Hive and Apache HCatalog projects.

The Hortonworks Data Platform 1.2 features a number of new enhancements designed to improve the enterprise viability of Apache Hadoop, including:

  • Simplified Hadoop Operations—Using the latest release of Apache Ambari, HDP 1.2 now provides both cluster management and the ability to zoom into cluster usage and performance metrics for jobs and tasks to identify the root cause of performance bottlenecks or operations issues. This enables Hadoop users to identify issues and optimize future job processing.
  • Improved Security and Multi-threaded Query—HDP 1.2 provides an enhanced security architecture and pluggable authentication model that controls access to Hive tables and the metastore. In addition, HDP 1.2 improves scalability by supporting multiple concurrent query connections to Hive from business intelligence tools and Hive clients.
  • Integration with High-performance Drivers Built for Big Data—HDP 1.2 empowers organizations with a trusted and reliable ODBC connector that enables the integration of current systems with high-performance drivers built for big data. The ODBC driver enables integration with reporting or visualization components through a SQL engine built into the driver. Hortonworks has partnered with Simba to deliver a trusted, reliable high-performance ODBC connector that is enterprise ready and completely free.
  • HBase Enhancements—By including and testing HBase 0.94.2, HDP 1.2 delivers important performance and operational improvements for customers building and deploying highly scalable interactive applications using HBase.

There goes the weekend!

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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