Posted On: Jul 29, 2020
HAQM Kinesis Data Firehose now supports streaming data delivery to MongoDB Cloud. This feature enables you to use a fully managed service to stream data to MongoDB Cloud without building custom applications or worrying about operating and managing the data delivery infrastructure. HAQM Kinesis Data Firehose incorporates error handling, auto-scaling, transformation, conversion, aggregation, and compression to help you accelerate the deployment of data streams across your organization.
HAQM Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics services. It can capture, transform, and deliver streaming data to HAQM S3, HAQM Redshift, HAQM Elasticsearch Service, generic HTTP endpoints, and service providers like MongoDB. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. With HAQM Kinesis Data Firehose, you don't need to write applications or manage resources.
MongoDB Cloud is a great way to build data-driven applications. The core of MongoDB Cloud is MongoDB Atlas, an elastic cloud database with built-in Search and Data Lake services for powering diverse workloads through a common API. The streaming delivery from Kinesis Data Firehose to MongoDB is powered by MongoDB Realm webhooks making it easier than ever to build robust, reactive data pipelines that stream directly into MongoDB collections, and enable real-time querying, enrichment, and analytics.
Visit the HAQM Kinesis Console to configure your data producers to send data to HAQM Kinesis Data Firehose and specify MongoDB Cloud as the destination. Once set, HAQM Kinesis Data Firehose takes care of reliable, scalable delivery of your streaming data to MongoDB. To learn more, explore the HAQM Kinesis Data Firehose developer guide.
For more information, read, Using MongoDB Realm WebHooks with AWS Kinesis Data Firehose Delivery Streams.
For HAQM Kinesis Data Firehose availability, refer to the AWS Region Table.
For more information on MongoDB, refer to MongoDB.