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Or as it’s more commonly being called: Week-ish in Review. Let’s recap on the latest – there’s some juicy technology goodness here.

Delivering on Stinger: Phase 1. Just this week, Hive 0.11 has been released. Owen (@owen_omalley) brought us the news that 55 – yes, fifty-five – developers from across the community have addressed 386 JIRA tickets and have delivered significant improvements to Hive along with an awesome demonstration of the power of community open-source development.…

And we are just about done with this week. But not quite – dig into the conversation from the past few days.

Hadoop Summit. We published the vast majority of sessions (70 so far) for the Hadoop Summit in San Jose, 26-27 June. The sessions stretch across 7 tracks from Architecture to Economics and we hope you can join us for THE Hadoop community event of the year. You can register here, and the schedule is here.…

Almost time to spend a relaxing weekend in the garden, or crushing some data in your garage-based homebrew Hadoop cluster – whichever you prefer. But before we choose our path, let’s take a look at the last two weeks of happenings (I was lost in Oregon last week).

Hadoop is the perfect app for OpenStack. While I was struggling with driving directions, Red Hat, Marantis and Hortonworks were announcing plans for Project Savanna which aims to automate the deployment of Hadoop on enterprise-class OpenStack-powered clouds. …

PORTLAND – The Rose city is a great place and this week it got even more interesting with the OpenStack Summit in town. I am more a data geek and very rarely do I venture down the stack into infrastructure, but wow, there is something cool going on with the OpenStack community.  I couldn’t help but to get wrapped up in the excitement.  Not only was the enthusiasm palpable, it was also very familiar.…

The convergence of big data and cloud is a disruptive market force that we at Hortonworks not only want to encourage but also accelerate. Our partnerships with Microsoft and Rackspace have been perfect examples of bringing Hadoop to the cloud in a way that enables choice and delivers meaningful value to enterprise customers. In January, Hortonworks joined the OpenStack Foundation in support of our efforts with Rackspace (i.e. OpenStack-based Hadoop solution for the public and private cloud).…

The end of another action-packed week and just before we all head off for the weekend then let’s have a recap on the conversations from this week – we hope you’re enjoying them.

We’re delighted by the response to our Hadoop Patterns of Use whitepaper and presentation - that really seems to have struck a chord with everyone thinking about what Hadoop can really do for their business. You can see that content just below here – an excellent read for the journey home.…

We’re cooking up some new tutorials for you to play with in your Hortonworks Sandbox to help you learn more about the Hortonworks Data Platform, Apache Hadoop, Hive, Pig and HCatalog, with maybe a smattering of Mahout in there as well.

More about Sandbox »

While you’re anxiously awaiting, we thought we’d give you some pointers to some resources so that you can experiment and play. After all, that’s what a Sandbox is all about, right?…

More of a 2 weeks in review this time around owing to the Easter break. So what’s been happening?

Falcon bringing Data Lifecycle Management for Hadoop. The big news this week was the newly approved Apache Software Foundation incubator project – Falcon. The project was initiated by the team at InMobi and engineers from Hortonworks towers with the intent of simplifying data management through a data lifecycle management framework. Something for everyone then. …

‘Big Data’ has become a hot buzzword, but a poorly defined one. Here we will define it.

Wikipedia defines Big Data in terms of the problems posed by the awkwardness of legacy tools in supporting massive datasets:

In information technology, big data[1][2] is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

It is better to define ‘Big Data’ in terms of opportunity, in terms of transformative economics.…

And the voting is over and the results are in for the Community Choice program of the Hadoop Summit San Jose 2013.

With over 300 sessions, and around 6000 users casting more than 15000 votes there was a lot of excitement to participate and influence the results - thanks to everyone for your contribution. At the end of the process, the selectees are:

  • Application and Data Science Track: Watching Pigs Fly with the Netflix Hadoop Toolkit (Netflix)
  • Deployment and Operations Track: Continuous Integration for the Applications on top of Hadoop (Yahoo!)
  • Enterprise Data Architecture Track: Next Generation Analytics: A Reference Architecture (Mu Sigma)
  • Future of Apache Hadoop Track: Jubatus: Real-time and Highly-scalable Machine Learning Platform (Preferred Infrastructure, Inc.)
  • Hadoop (Disruptive) Economics Track: Move to Hadoop, Go Fast and Save Millions: Mainframe Legacy Modernization (Sears Holding Corp.)
  • Hadoop-driven Business / BI Track: Big Data, Easy BI (Yahoo!)
  • Reference Architecture Track: Genie – Hadoop Platformed as a Service at Netflix (Netflix)

Congratulations to the selectees for each track, and a further honorable mention to Sears for winning the ‘Longest Session Title So Far’ which was a surprisingly hard fought contest!…

We want to take a moment to thank everyone who attended the Hadoop Summit in Amsterdam - THANK YOU! With nearly 500 people registered for the event we think we can safely say is was a big success. We’ve had overwhelming support to do it again next year – so watch this space.

The awesome Beurs Van Berlage venue set us up for a series of fantastic conversations and really well attended sessions and talks as Hadoop continues to explode onto the enterprise scene .…

There have been many Apache Hadoop-related announcements the past few weeks, making it difficult to separate the signal from the marketing noise. One thing is crystal clear however… there is a large and growing appetite for Enterprise Hadoop because it helps unlock new insights and business opportunities in a way that was not previously technologically or economically feasible.

Enterprise and Open Source are NOT Mutually Exclusive

Dan Woods from Forbes, recently penned an article entitled “Why SQL Matters, the Limits of Open Source, and Other Lessons of EMC Greenplum’s Pivotal HD” where he paints a picture of enterprise and open source in opposite corners.…

 

In Derrick Harris’ article on GigaOM entitled “EMC to Hadoop competition: See ya, wouldn’t wanna be ya.”, EMC unveiled their new Pivotal HD offering which effectively re-architects the Greenplum analytic database so it sits on top of the Hadoop Distributed File System (HDFS). Scott Yara, Greenplum cofounder, is excited about the new product. Since a key focus for us at Hortonworks is to deeply integrate Hadoop with other data systems (a la our efforts with Teradata, Microsoft, MarkLogic, and others), I’m always excited to see data system providers like Greenplum decide to store their data natively in HDFS.…

Last week, the HBase community released 0.94.5, which is the most stable release of HBase so far. The release includes 76 jira issues resolved, with 61 bug fixes, 8 improvements, and 2 new features.

Most of the bug fixes went against the REST server, replication, region assignment, secure client, flaky unit tests, 0.92 compatibility and various stability improvements. Some of the interesting patches in this release are:
[HBASE-3996] – Support multiple tables and scanners as input to the mapper in map/reduce jobs
[HBASE-5416] – Improve performance of scans with some kind of filters.…

YARN is part of the next generation Hadoop cluster compute environment. It creates a generic and flexible resource management framework to administer the compute resources in a Hadoop cluster. The YARN application framework allows multiple applications to negotiate resources for themselves and perform their application specific computations on a shared cluster. Thus, resource allocation lies at the heart of YARN.

YARN ultimately opens up Hadoop to additional compute frameworks, like Tez, so that an application can optimize compute for their specific requirements.…

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