THE BRIGHT BLOG

There's more to big data than Hadoop. If your plans call for high performance data crunching, you may be thinking of deploying Apache Spark, but setting up and running a Spark cluster can be challenging. Bright Cluster Manager makes it easy by giving you a comprehensive solution for deploying, monitoring, and managing Spark. 

Version 7.3 of Bright OpenStack has a lot of great features. One I am most excited about is Cluster on Demand (COD), which lets end users provision entire cluster environments in a Bright OpenStack cloud. Any kind of cluster can be implemented, including HPC, Hadoop, Spark, or even a Bright OpenStack cloud.

As you’ve probably heard by now, Apache Spark™ is a fast general processing engine which can be used for large-scale data processing. Bright Cluster Manager has supported Spark since version 7.1, but a number of recent enhancements were made to the Bright support for Spark in Version 7.2 which improve functionality and ease of use for our users.

If you are curious to know more about Apache Spark™ and how it can be used for large-scale data processing, check out our latest vodcast series. As a fast and general processing engine compatible with Hadoop data, Spark can run in Hadoop clusters through YARN or in a standalone mode. For both batch processing and new workloads like streaming, interactive queries, and machine learning, Spark has a lot going for it.

Apache Spark™ lives up to its tagline of ‘Lightning Fast Cluster Computing,’ according to Bright Computing’s Ian Lumb, who discussed this ‘hot’ cluster computing framework in our newly released vodcast. The vodcast, titled ‘The Rise in Popularity of Apache Spark,’ includes Ian’s take on the meteoric rise in popularity of Spark, comparisons between Spark and Hadoop, the importance of Spark’s excellent handling of converged analytics, and the exciting potential intersection of Spark and HPC. Take a listen to learn more about the unique solution that has ignited so much interest in the world of Big Data and beyond.

Spark'ing Scope Creep

I had the fortunate opportunity to present in the disruptive-technology track at the 2015 Rice Oil and Gas HPC Workshop during the first week of March. What I presented during my two-minute drill in this session ended up being much more disruptive than I anticipated.

Shape the program of the Hadoop Summit Europe 2015 with your votes. Is there a better way to demonstrate community? We think not!

The Summit features 6 tracks. By tapping the expertise of our Hadoop braintrust, we’ve submitted 4 ideas to 3 different tracks. Because we’d like to earn your votes, please allow us elaborate.

Pop quiz: How many steps does it take to upgrade your Hadoop distribution?

Choose one answer:

Last week we released support for Apache Hadoop 2.5.0. I thought it would be a good time to revisit all the updates we’ve made over the last 6 months - in case you missed them.

Latest Updates

Our latest updates include:

• Apache Hadoop 2.5.1  Typo? Not exactly. We introduced support for Apache Hadoop 2.5.0 last week. Since Version 2.5.1 (released on September 13, 2014) is a minor release that builds upon the stable 2.4.1 release, you can now expect support via YUM updates to Bright. Note that vanilla Apache Hadoop includes HDFS (the Hadoop Distributed File System), ZooKeeper (the coordination service) as well as YARN (the workload manager).
• Cloudera CDH 5.1.0  We introduced support for CDH 5.1.0 in mid-July.
• Hortonworks HDP 2.1  We introduced support for Hortonworks Data Platform (HDP) 2.1 in mid-May.

Bottom line: Bright Cluster Manager maintains support for all major distributions of Apache Hadoop. Because we’re obsessed with keeping you current, you don’t have to be. We free you up to focus on the analysis of Big Data - and wasn’t that why you got interested in Hadoop in the first place?