The Hortonworks Blog

Posts categorized by : Apache Hadoop

Hadoop 2 and its YARN-based architecture has increased the interest in new engines to be run on Hadoop and one such workload is in-memory computing for machine learning and data science use cases. Apache Spark has emerged as an attractive option for this type of processing and today, we announce availability of our HDP 2.1 Tech Preview Component of Apache Spark.  This is a key addition to the platform and brings another workload supported by YARN on HDP.…

The first use of the term BoF session was used at the Digital Equipment Users’ Society (DECUS) conference in the 1960s. Its essence was to bring together like minds and thought leaders—just as birds of the feather flock together— to share and exchange computing ideas, in an informal yet spirited way. Since then, the organizers and sponsors of most computing conferences have been loyal to its essence and spirit.

For ideas and innovation happen in collaboration—not in isolation. …

This is the second in our series on the motivations and architecture for improvements to the Apache Hadoop YARN’s Resource Manager Restart resiliency. Other in the series are:

Introduction: Phase I – Preserve Application-queues

In the introductory blog, we previewed what RM Restart Phase I entails. In essence, we preserve the application-queue state into a persistent store and reread it upon RM restart, eliminating the need for users to resubmit their applications.…

This is the first post in our series on the motivations and architecture for improvements to the Apache Hadoop YARN’s Resource Manager Restart resiliency. Other in the series are:

Resource Manager (RM) is the central authority of Apache Hadoop YARN for resource management and scheduling. It is responsible for allocation of resources to applications like Hadoop MapReduce jobs, Apache TEZ DAGs, and other applications running atop YARN.…

The Apache Knox Gateway team is pleased to announce Knox’s first release as an Apache top-level project: Apache Knox Gateway 0.4.0. The team resolved approximately 100 JIRAs for this release and Knox Gateway is now better positioned to provide complete security for REST API access to a Hadoop cluster.

The new features in Knox Gateway 0.4.0 are the features that enterprise security officers expect in a gateway solution:

  • Perimeter security for a Hadoop cluster
  • Support for enterprise group lookup
  • Audit log of all gateway activity
  • Command line tooling for CMF provisioning
  • Protection for web application vulnerabilities
  • Pre-authentication via SSO token
  • And many more…

As a top-level project, Apache Knox Gateway is fully endorsed by the Apache Software Foundation, and this improves coordination between development of Knox and the other core Hadoop projects with which it interacts.…

Yesterday the Apache Ambari community proudly released version 1.5.1. This is the result of constant, concerted collaboration among the Ambari project’s many members. This release represents the work of over 30 individuals over 5 months and, combined with the Ambari 1.5.0 release, resolves more than 1,000 JIRAs.

This version of Ambari makes huge strides in simplifying the deployment, management and monitoring of large Hadoop clusters, including those running Hortonworks Data Platform 2.1.…

The Apache Hive community has voted on and released version 0.13 today. This is a significant release that represents a major effort from over 70 members who worked diligently to close out over 1080 JIRA tickets.

Hive 0.13 also delivers the third and final phase of the Stinger Initiative, a broad community based initiative to drive the future of Apache Hive, delivering 100x performance improvements at petabyte scale with familiar SQL semantics.…

The power of a well-crafted speech is indisputable, for words matter—they inspire to act. And so is the power of a well-designed Software Development Kit (SDK), for high-level abstractions and logical constructs in a programming language matter—they simplify to write code.

In 2007, when Chris Wensel, the author of Cascading Java API, was evaluating Hadoop, he had a couple of prescient insights. First, he observed that finding Java developers to write Enterprise Big Data applications in MapReduce will be difficult and convincing developers to write directly to the MapReduce API was a potential blocker.…

The third HBaseCon is happening in May 5th this year in San Francisco which is THE community event for Apache HBase. As with the previous years, this year the agenda is quite exciting.

There will be 4 tracks, Operations, Features and Internals, Ecosystem and Case Studies. The keynotes will include speakers from Cloudera who is the event host, Google BigTable team as a follow up to their ‘06 BigTable paper, Salesforce on their experience with HBase operations and use cases and Facebook on their strongly consistent multi data center replication scheme.…

As the Red Hat Summit shifts to the west coast in San Francisco this year Hortonworks and Red Hat will be demonstrating the progress of our engineering efforts. Our engineers have been hard at work in the factories and in the communities deeply integrating our open source offerings to create a comprehensive platform for new analytic applications. As a reminder in February Red Hat and Hortonworks announced a comprehensive open source initiative to deliver infrastructure solutions to bring 100-percent open source Hadoop to the hybrid cloud.…

It gives me great pleasure to announce that the Apache Hadoop community has voted to release Apache Hadoop 2.4.0! Thank you to every single one of the contributors, reviewers and testers!

The community fixed 411 JIRAs for 2.4.0 (on top of the 511 JIRAs resolved for 2.3.0). Of the 411 fixes:

  • 50 are in Hadoop Common,
  • 171 are in HDFS,
  • 160 are in YARN and
  • 30 went into MapReduce

Hadoop 2.4.0 is the second Hadoop release in 2014, following Hadoop 2.3.0’s February release and its key enhancements to HDFS such as Support for Heterogeneous Storage and In-Memory Cache.…

We are excited to announce that the Apache™ Tez community voted to release version 0.4 of the software.

Apache Tez is an alternative to MapReduce that provides a powerful framework for executing a complex topology of tasks for data access in Hadoop. Version 0.4 incorporates the feedback from extensive testing of Tez 0.3, released just last month.

This release is especially meaningful because it coincides with completion of the Stinger Initiative (a collaborative community effort involving 145 developers across 44 companies) and the upcoming release of Apache Hive 0.13.…

Securing any system requires you to implement layers of protection.  Access Control Lists (ACLs) are typically applied to data to restrict access to data to approved entities. Application of ACLs at every layer of access for data is critical to secure a system. The layers for hadoop are depicted in this diagram and in this post we will cover the lowest level of access… ACLs for HDFS.

This is part of the HDFS Developer Trail series.  …

Today we are proud to announce that the formation of a terrific partnership with LucidWorks to bring search to the Hortonworks Data Platform. LucidWorks delivers an enterprise-grade search development platform built atop the power of Apache Solr.

Presentation & Applications Enable both existing and new applications to provide value to the organization. Enterprise Management & Security Empower existing operations and security tools to manage Hadoop. Governance Integration Data Workflow, Lifecycle & Governance Data Access Access your data simultaneously in multiple ways (batch, interactive, real-time) Script SQL Java/Scala NoSQL Stream Search In-Mem Others...…

If you’re excited to get started with the new features in Hortonworks Data Platform 2.1, then we’ve included 4 tutorials for you try out – Sandbox-style.

You can download the HDP 2.1 Technical Preview here, and then get stuck into these great tutorials.

Interactive Query with Apache Hive and Apache Tez

OK, so you’re not going to get huge performance out of a one-node VM, but you can try out Hive on Tez, and see the performance gains versus MapReduce, and also try out features such as Vectorized Query, and the host of new SQL features.…

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