Cloudera Engineering Blog · Hadoop Posts
Cloudera’s culture is premised on innovation and teamwork, and there’s no better example of them in action than our internal hackathon.
Cloudera Engineering doubled-down on its “hackathon” tradition last week, with this year’s edition taking an around-the-clock approach thanks to the HQ building upgrade since the 2013 edition (just look at all that space!).
The number of powerful data query tools in the Apache Hadoop ecosystem can be confusing, but understanding a few simple things about your needs usually makes the choice easy.
Ah, the good old days. I recall vividly that in 2007, I was faced to store 1 billion XML documents and make them accessible as well as searchable. I had few choices on a given shoestring budget: build something one my own (it was the rage back then—and still is), use an existing open source database like PostgreSQL or MySQL, or try this thing that Google built successfully and that was now implemented in open source under the Apache umbrella: Hadoop.
The combination of OpenShift and Kite SDK turns out to be an effective one for developing and testing Apache Hadoop applications.
At Cloudera, our engineers develop a variety of applications on top of Hadoop to solve our own data needs (here and here). More recently, we’ve started to look at streamlining our development process by using a PaaS (Platform-as-a-Service) for some of these applications. Having single-click deployment and updates to consistent development environments lets us onboard new developers more quickly, and helps ensure that code is written and tested along patterns that will ensure high quality.
Cloudera Enterprise 5.2 contains new functionality for security, cloud deployments, and real-time architectures, and support for the latest open source component releases and partner technologies.
We’re pleased to announce the release of Cloudera Enterprise 5.2 (comprising CDH 5.2, Cloudera Manager 5.2, Cloudera Director 1.0, and Cloudera Navigator 2.1).
Thanks to M. Asokan, Chief Architect at Syncsort, for the guest post below.
Apache Sqoop provides a framework to move data between HDFS and relational databases in a parallel fashion using Hadoop’s MR framework. As Hadoop becomes more popular in enterprises, there is a growing need to move data from non-relational sources like mainframe datasets to Hadoop. Following are possible reasons for this:
Using this new tutorial alongside Cloudera Live is now the fastest, easiest, and most hands-on way to get started with Hadoop.
At Cloudera, developer enablement is one of our most important objectives. One only has to look at examples from history (Java or SQL, for example) to know that knowledge fuels the ecosystem. That objective is what drives initiatives such as our community forums, the Cloudera QuickStart VM, and this blog itself.
With 1.4, Impala’s performance lead over the SQL-on-Hadoop ecosystem gets wider, especially under multi-user load.
As noted in our recent post about the Impala 2.x roadmap (“What’s Next for Impala: Focus on Advanced SQL Functionality”), Impala’s ecosystem momentum continues to accelerate, with nearly 1 million downloads since the GA of 1.0, deployment by most of Cloudera’s enterprise data hub customers, and adoption by MapR, Amazon, and Oracle as a shipping product. Furthermore, in the past few months, independent sources such as IBM Research have confirmed that “Impala’s database-like architecture provides significant performance gains, compared to Hive’s MapReduce- or Tez-based runtime.”
The meetup opportunities during the conference week are more expansive than ever — spanning Impala, Spark, HBase, Kafka, and more.
Strata + Hadoop World 2014 is a kaleidoscope of experiences for attendees, and those experiences aren’t contained within the conference center’s walls. For example, the meetups that occur during the conf week (which is concurrent with NYC DataWeek) are a virtual track for developers — and with Strata + Hadoop World being bigger than ever, so is the scope of that track.
What does a “Big Data engineer” do, and what does “Big Data architecture” look like? In this post, you’ll get answers to both questions.
Apache Hadoop has come a long way in its relatively short lifespan. From its beginnings as a reliable storage pool with integrated batch processing using the scalable, parallelizable (though inherently sequential) MapReduce framework, we have witnessed the recent additions of real-time (interactive) components like Impala for interactive SQL queries and integration with Apache Solr as a search engine for free-form text exploration.
Impala 2.0 will add much more complete SQL functionality to what is already the fastest SQL-on-Hadoop solution available.
In September 2013, we provided a roadmap for Impala — the open source MPP SQL query engine for Apache Hadoop, which was on release 1.1 at the time — that documented planned functionality through release 2.0 and beyond.