Spark-MongoDB
A Spark library for reading and writing data between Spark SQL and MongoDB collections.
What is Spark-MongoDB?
Spark-MongoDB is a library that provides a connector between Apache Spark and MongoDB, enabling bidirectional data flow between Spark SQL and MongoDB collections. It solves the problem of integrating MongoDB's document database with Spark's distributed processing framework, allowing users to query MongoDB data using Spark SQL and write processed results back to MongoDB.
Data engineers and data scientists working with both Spark and MongoDB who need to perform ETL, analytics, or machine learning on data stored in MongoDB collections.
Developers choose Spark-MongoDB because it provides a native-feeling integration with Spark's DataFrame API, supports multiple programming languages, and eliminates the need for custom connectors when working with MongoDB data in Spark workflows.
Overview
Spark library for easy MongoDB access
Use Cases
Best For
- Performing distributed analytics on MongoDB data using Spark
- Building ETL pipelines that extract data from MongoDB for processing in Spark
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