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This post is the second in our series on the motivations, architecture and performance gains of Apache Tez for data processing in Hadoop. The series has the following posts:

Overview

Apache Tez models data processing as a dataflow graph, with the vertices in the graph representing processing of data and edges representing movement of data between the processing.…

With HDP 1.3 and HDP 2.0 Beta, we introduced the ability to create snapshots to protect important enterprise data sets from user or application errors.

HDFS Snapshots are read-only point-in-time copies of the file system. Snapshots can be taken on a subtree of the file system or the entire file system and are:

  • Performant and Reliable: Snapshot creation is atomic and instantaneous, no matter the size or depth of the directory subtree
  • Scalable: Snapshots do not create extra copies of blocks on the file system.

He loves me, he loves me not… using daisies to figure out someone’s feelings is so last century. A much better way to determine whether someone likes you, your product or your company is to do some analysis on Twitter feeds to get better data on what the public is saying. But how do you take thousands of tweets and process them?  We show you how in our video – Understand your customers’ sentiments with Social Media Data – that you can capture a Twitter stream to do Sentiment Analysis.…

We’re continuing our series of quick interviews with Apache Hadoop project committers at Hortonworks.

This week Venkat Ranganathan discusses using Apache Sqoop for bulk data movement between Hadoop and enterprise data stores. Sqoop can also move data the other way, from Hadoop into an EDW.

Venkat is a Hortonworks engineer and Apache Sqoop committer who wrote the connector between Sqoop and the Netezza data warehousing platform. He also worked with colleagues at Hortonworks and in the Apache community to improve integration between Sqoop and Apache HCatalog, delivered in Sqoop 1.4.4.…

As part of HDP 2.0 Beta, YARN takes the resource management capabilities that were in MapReduce and packages them so they can be used by new engines.  This also streamlines MapReduce to do what it does best, process data.  With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management.

In this blog post we’ll walk through how to plan for and configure processing capacity in your enterprise HDP 2.0 cluster deployment.…

The upcoming Hive 0.12 is set to bring some great new advancements in the storage layer in the forms of higher compression and better query performance.

Higher Compression

ORCFile was introduced in Hive 0.11 and offered excellent compression, delivered through a number of techniques including run-length encoding, dictionary encoding for strings and bitmap encoding.

This focus on efficiency leads to some impressive compression ratios. This picture shows the sizes of the TPC-DS dataset at Scale 500 in various encodings.…

The Stinger Initiative is Hortonworks’ community-facing roadmap laying out the investments Hortonworks is making to improve Hive performance 100x and evolve Hive to SQL compliance to simplify migrating SQL workloads to Hive.

We launched the Stinger Initiative along with Apache Tez to evolve Hadoop beyond its MapReduce roots into a data processing platform that satisfies the need for both interactive query AND petabyte scale processing. We believe it’s more feasible to evolve Hadoop to cover interactive needs rather than move traditional architectures into the era of big data.…

We hosted a webinar on YARN a couple of weeks ago (see the slides and playback here). As you might expect, there was a lot of great questions and here is a set of answers to those questions.

Our next YARN-oriented Office Hours online on Sept 11th at 2pm PST. Join us on Meetup!

Who is using YARN and what benefits have they received from it?

On great public example of in production use of YARN, is at Yahoo!.…

Another week, another release…  Following the release of Apache Hadoop 2.0 beta last week, we are excited to release the beta of Hortonworks Data Platform 2.0, the first commercial release of the stable YARN API and protocols on which new applications can now be built.

For our customers this is a great opportunity to ensure the release meets expectations and provides a vehicle to voice feedback that will work to improve Hadoop and shape its roadmap. …

This post is authored by Jian He with Vinod Kumar Vavilapalli and is the seventh post in the multi-part blog series on Apache Hadoop YARN – a general-purpose, distributed, application management framework that supersedes the classic Apache Hadoop MapReduce framework for processing data in Hadoop clusters. Other posts in this series:

Introduction

Apache Hadoop 2 is in beta now .…

In the last 60 seconds there were 1,300 new mobile users and there were 100,000 new tweets. As you contemplate what happens in an internet minute Amazon brought in $83,000 worth of sales. What would be the impact of you being able to identify:

  • What is the most efficient path for a site visitor to research a product, and then buy it?
  • What products do visitors tend to buy together, and what are they most likely to buy in the future?

Continuing our series of quick interviews with Apache Hadoop project committers and contributors at Hortonworks.

To follow on from yesterday’s Server Log processing with Apache Flume tutorial we talk with Roshan Naik, Hortonworks engineer and Apache Flume contributor, about what Flume is, how it works and where it’s going.

Learn more about Flume here or at the Apache Hadoop project site.

When they’re not planning to overthrow their human overlords, most servers can be found spewing out vast amounts of data in the form of server logs. As we showed in our video - Deliver responsive IT from events in Server Logs - these logs contain a lot of value.

So if you fire up the Hortonworks Sandbox today, you’ll be delighted to find Tutorial 12: Refining and Visualizing Server Log Data as a step-by-step guide to the video. …

This post authored by Zhijie Shen with Vinod Kumar Vavilapalli.

This is the sixth blog in the multi-part series on Apache Hadoop YARN – a general-purpose, distributed, application management framework that supersedes the classic Apache Hadoop MapReduce framework for processing data in Hadoop clusters. Other posts in this series:

Introducing Apache Hadoop YARN
Apache Hadoop YARN – Background and an Overview
Apache Hadoop YARN – Concepts and Applications
Apache Hadoop YARN – ResourceManager
Apache Hadoop YARN – NodeManager

Introduction

The beta release of Apache Hadoop  2.x has finally arrived and we are striving hard to make the release easy to adopt with no or minimal pain to our existing users.…

It’s my great pleasure to announce that the Apache Hadoop community has declared Hadoop 2.x as Beta with the vote closing over the weekend for the hadoop-2.1.0-beta release.

As noted in the announcement to the mailing lists, this is a significant milestone across multiple dimensions: not only is the release chock-full of significant features (see below), it also represents a very stable set of APIs and protocols on which we can continue to build for the future.…

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