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Hadoop weekly news February 2014

BeyeNetwork

February 3, 2014

BeyeNetwork has a post on an often overlooked differentiator between SQL-on-Hadoop systems and proprietary database systems. In proprietary systems, the query engine, storage format, and file systems are typically tightly coupled. Conversely, many of the SQL-on-Hadoop systems use the Hive Metastore to find data on HDFS and for discovering storage formats. Since the storage layer and storage formats are decoupled from the query engines, it’s easy to switch between e.g. Impala and Hive and Presto.

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MapR supports 5 different SQL-on-Hadoop technologies
February 3, 2014

MapR supports 5 different SQL-on-Hadoop technologies as part of their distribution. Given the large number of possible solutions, MapR has posted a detailed comparison to help users choose the best technology for their problem. The page covers Hive, Drill, Impala, Presto, and Shark across categories like SQL completeness and UDF support.

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Cloudera Releases
February 3, 2014

Cloudera has released Cloudera Manager 4.8.1. In addition to resolving several issues, the release adds the ability to distribute Apache Spark (incubating) via parcels on a CDH 4 cluster.

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Deploying a Hadoop Cluster on Amazon EC2 with HDP2
February 3, 2014

In this post, we’ll walk through the process of deploying an Apache Hadoop 2 cluster on the EC2 cloud service offered by Amazon Web Services (AWS), using Hortonworks Data Platform
Both EC2 and HDP offer many knobs and buttons to cater to your specific, performance, security, cost, data size, data protection and other requirements. I will not discuss most of these options in this blog as the goal is to walk through one particular path of deployment to get started.

Elasticsearch 0.90.11 and 1.0.0.RC2 released
February 3, 2014

We are happy to announce the release of Elasticsearch 0.90.11 and Elasticsearch 1.0.0.RC2, both of which are based on Lucene 4.6.1.

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Spark is Now Generally Available for Cloudera Enterprise
February 3, 2014

Cloudera is announcing the general availability of support for Spark, bringing interactive machine learning and stream processing to enterprise data hubs.

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