Build new classes of sophisticated, real-time analytics by combining Apache Spark, the industry's leading data processing engine, with MongoDB, the industry’s fastest growing database. The MongoDB Connector for Apache Spark is generally available, certified, and supported for production usage today. Sign up for the free MongoDB University course to get you on the fast track to your next data science project.
Access Insights Now
We live in a world of “big data”. But it isn’t just the data itself that is valuable – it’s the insight it can generate. How quickly an organization can unlock and act on that insight has become a major source of competitive advantage. Collecting data in operational systems and then relying on nightly batch extract, transform, load (ETL) processes to update the enterprise data warehouse (EDW) is no longer sufficient.
Unlock the Power of Apache Spark
The MongoDB Connector for Apache Spark exposes all of Spark’s libraries, including Scala, Java, Python and R. MongoDB data is materialized as DataFrames and Datasets for analysis with machine learning, graph, streaming, and SQL APIs.
Leverage the Power of MongoDB
The MongoDB Connector for Apache Spark can take 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. This is very different from simple NoSQL datastores that do not offer secondary indexes or in-database aggregations. In these cases, Spark would need to extract all data based on a simple primary key, even if only a subset of that data is required for the Spark process. This means more processing overhead, more hardware, and longer time-to-insight for data scientists and engineers.
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.
Building an artificial intelligence (AI) application requires huge amounts of data to be processed at once, both reliably and efficiently. To store all that data, we use MongoDB for its flexible data model and its scaling capabilities. And to process all of that data to build machine learning models, we build robust pipelines in Scala using the distributed data processing capabilities of Spark. Now, with the new native MongoDB Connector for Apache Spark, we have an even better way of connecting up these two key pieces of our infrastructure.
The MongoDB Spark Connector is available for download from GitHub
Read our new whitepaper: Turning Analytics into Real Time Action with Apache Spark and MongoDB
Read MongoDB Spark Connector documentation here
Sign up for the free Apache Spark course from MongoDB University