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MongoDB Enables Advanced Real-Time Analytics on Fast Moving Data with New Connector for Apache SparkCompany's Database Recognized as Application Certified by Databricks NEW YORK, June 28, 2016 /PRNewswire/ -- MongoDB World -- MongoDB, the database for giant ideas, today announced MongoDB Connector for Apache Spark, a powerful integration that enables developers and data scientists to create new insights and drive real-time action on live, operational, and streaming data. The MongoDB Connector for Apache Spark is now generally available and ready for production usage. Working closely with Databricks, the company founded by the team that created the Apache Spark project, the MongoDB Connector has received Databricks Certified Application status for Spark. The certification means that developers can focus on building modern, data driven applications, knowing that the connector provides seamless integration and complete API compatibility between Spark processes and MongoDB. "Combining Apache Spark, the leading open-source big data analytics processing engine in the Apache Software Foundation, with MongoDB, the industry's fastest-growing database, enables organizations to fully realize the potential of real-time analytics," said Eliot Horowitz, co-founder and CTO of MongoDB. "Spark jobs can be executed directly against operational data managed by MongoDB, without the time and expense of Extract Transform Load (ETL) processes. MongoDB can efficiently index and serve analytics results back into live, operational processes, making them smarter, more contextual and responsive to events as they happen." Delivering Faster, Lower Cost Performance for Advanced Analytics "Users are already combining Apache Spark and MongoDB to build sophisticated analytics applications. The new native MongoDB Connector for Apache Spark provides higher performance, greater ease of use, and access to more advanced Apache Spark functionality than any MongoDB connector available tody," said Reynold Xin, co-founder and chief architect of Databricks. Written in Scala, Apache Spark's native language, the connector provides a more natural development experience for Spark users. The connector exposes all of Spark's libraries, enabling MongoDB data to be materialized as DataFrames and Datasets for analysis with machine learning, graph, streaming and SQL APIs, further benefiting from automatic schema inference. The connector also takes advantage of MongoDB's aggregation pipeline and rich secondary indexes to extract, filter, and process only the range of data it needs – for example, analyzing all customers located in a specific geography. To maximize performance across large, distributed data sets, the MongoDB Connector for Apache Spark can co-locate Resilient Distributed Datasets (RDDs) with the source MongoDB node, thereby minimizing data movement across the cluster and reducing latency. Users Eager to Realize Potential of Real-Time Analytics with MongoDB Users can get started learning how to leverage the new connector with a free MongoDB University Course, Getting Started with Spark and MongoDB. Resources
About MongoDB Press Contacts MongoDB, International Logo - http://photos.prnewswire.com/prnh/20160627/384058LOGO To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/mongodb-enables-advanced-real-time-analytics-on-fast-moving-data-with-new-connector-for-apache-spark-300290985.html SOURCE MongoDB |