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.
Key features of Elasticsearch for Apache Hadoop 2.1
- Native integration with all GA releases of Spark and Spark SQL (1.0-1.4), including push down translation of Spark SQL to Elasticsearch Query DSL
- Native integration with Apache Storm, providing the ability to read/write data to Elasticsearch via Spout (read streams) and Bolt (write streams)
- Certification with CDH5.x, Databricks Spark, HDP2.x, and MapR 4.x
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