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Redis Data Integration Multi-Source and Multi-Pipeline

2 posts · Jun 2026 – Sep 2026

What this trail covers

Redis Data Integration (RDI) in Redis Cloud on AWS is now generally available. RDI is a managed service for moving operational and analytical data into Redis in near real-time, keeping it in sync with source databases and data warehouses. It supports initial bulk hydration and ongoing CDC, transforming source records into Redis data structures optimized for sub-millisecond reads. Key improvements include better CDC latency and throughput, faster full hydration, self-service pipeline management with early validation, advanced source/processor configuration, support for snapshot SQL statements, new source support for Snowflake (preview) and MongoDB, and CAPI support for API and Terraform provisioning. RDI is a foundational building block for Redis Iris, enabling agents to retrieve real-time context from systems of record.

All Redis trails
  1. Multi-source and multi-pipeline RDI: one install instead of manyThis post introduces multi-source pipelines for Redis Data Integration (RDI), enabling a single RDI pipeline to ingest data from multiple source databases into a single Redis target. It also announces upcoming multi-pipeline support, where a single RDI installation can manage multiple RDI pipelines, simplifying the operation of complex integration architectures. The post highlights use cases such as consolidating data for fraud prevention, unifying regional or tenant-specific data, building consolidated portfolio views, and supporting federated-cache architectures.
  2. Redis Data Integration in Redis Cloud is now GA in AWS | RedisThis post announces the General Availability of Redis Data Integration (RDI) in Redis Cloud on AWS. It highlights new source support for Snowflake (preview) and MongoDB, improved CDC latency and throughput, faster full hydration, enhanced pipeline management with early validation and clearer states, support for snapshot SQL statements, and CAPI integration for automation. The post emphasizes RDI's role in transforming data into a fast data layer for modern apps, real-time decisioning, and AI workloads, particularly for feature stores and reverse ETL scenarios powering agents.