Elastic Adds Support for Spark, Spark SQL, and Storm to Hadoop Connector

Date 2015/7/1 8:01:29 | Topic: Product News

Elastic, the company behind the popular open source projects Elasticsearch, Logstash, and Kibana with more than 20 million downloads, today released version 2.1 of its Hadoop connector, Elasticsearch for Apache Hadoop, adding support for Spark, Spark SQL, and Storm.
While Hadoop provides value as a high-scale, low-cost storage medium for doing batch analytics, it was not designed for real-time data availability or responsive queries. Elasticsearch for Apache Hadoop makes it simple for businesses to index, query, and backup data from Hadoop into Elasticsearch, adding the capabilities of a scalable, real-time search and analytics engine to their Hadoop platform -- whether it's for internal or external applications, or for performing back-office system monitoring, security, and log analysis. With the release of Elasticsearch for Apache Hadoop 2.1, Elastic's Hadoop connector now supports Spark, Spark SQL, and Storm, in addition to native integration with MapReduce, Hive, Pig, and Cascading. It is also certified with Cloudera, Databricks, Hortonworks, and MapR.

"We see Hadoop being used more and more in our customer base, especially in industries that deal with vast amounts of varied data, like financial services, healthcare, and telecommunications. With added support for Spark, Spark SQL, and Storm, Elasticsearch for Apache Hadoop 2.1 provides Elastic's rich search and analytics to the next-generation run-times in the Hadoop ecosystem," said Costin Leau, Hadoop engineering lead at Elastic.





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