EnterpriseDB® (EDB™), the database platform company for digital business, announced the general availability of a new version of the EDB Postgres Data Adapter for Hadoop with compatibility for the Apache Spark cluster computing framework. The new version gives organizations the ability to combine analytic workloads based on the Hadoop Distributed File System (HDFS) with operational data in Postgres, using an Apache Spark interface.
It is a competitive requirement that organizations be able to leverage Big Data to obtain insight and actionable intelligence within the context of their business transactions,” said Lenley Hensarling, Senior Vice President, Strategy and Product Management at EnterpriseDB. “The EDB Postgres Data Adapter for Hadoop now makes it possible for organizations to leverage Apache Spark to gain insight from the large trove of data held in HDFS.”
As the demand for real-time intelligence has increased, more organizations have turned to Apache Spark for its ability to process data significantly faster than MapReduce, the processing component in Hadoop. Apache Spark data persists in-memory on the processing framework, producing speeds exponentially faster for some analytics. Complex applications that require multiple operations, including analytics on streaming data, benefit from using Apache Spark, as well as real-time marketing, cybersecurity analytics, machine log monitoring, and online recommendations.
The EDB Postgres Data Adapter that EnterpriseDB developed and released for the Postgres user community is a Foreign Data Wrapper (FDW) for Hadoop with Apache Spark compatibility. FDWs act as pipelines between Postgres databases and external data sources. They allow PostgreSQL queries to include structured or unstructured data, from multiple sources such as Postgres and NoSQL databases, as well as HDFS, as if they were in a single database.
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