Redis Data Integration (RDI) is a product that helps Redis Enterprise users ingest data in near real-time, so that Redis becomes part of their data fabric without additional integration efforts.

RDI currently supports these scenarios:

  • Ingest scenario. RDI mirrors the application’s primary database to Redis using a Capture Data Change (CDC) tool. RDI transforms the database model and types to Redis model and types. This scenario is useful when the application database is not performant and scalable enough to serve the read queries. RDI helps to offload all read queries to Redis.

    Ingest flow

  • Write-behind scenario (Preview). RDI applies data changes in Redis to one or more downstream data stores. RDI can map and transform the Redis types and models to the downstream types and models. This scenario is useful when the application needs fast writes and reads for some of the queries but has to provide data to other downstream services that need them in different models for other uses.

    Write-behind flow

To learn more see Architecture for more details and architecture.

Supported sources (ingest)

Redis Data Integration supports the following database sources using Debezium Server connectors:

Database Versions
Oracle 12c, 19c, 21c
MariaDB >= 10.5
MySQL 5.7, 8.0.x
Percona XtraDB 5.7, 8.0.x
Postgres 10, 11, 12, 13, 14, 15
SQL Server 2017, 2019
Cassandra >= 3.0
Datastax Cassandra >= 6.8.0

Supported targets (write-behind)

Database
Oracle
MariaDB
MySQL
Postgres
SQL Server
Cassandra

Features

RDI is an enterprise-grade product with an extensive set of features:

Performance and scalability

  • Up to 2 seconds from source to target
  • Multi-shard support (each shard supports 28K ops/sec)

Resiliency, high availability, and data delivery guarantees

  • At least once guarantee, end to end
  • Data in transit is replicated to replica a shard
  • Data persistence (Redis AOF)
  • Back-pressure mechanism preventing cascading failure
  • Reconnect on failure and write retries

Developer tools and data transformation

  • Declarative data filtering, mapping and transformations
  • Support for SQL and JMESPath expressions in transformations
  • Additional JMESPath custom functions simplifying transformation expressions
  • Transformation jobs validation
  • Zero downtime pipeline reconfiguration
  • Hard failures routing to dead-letter queue stream for troubleshooting
  • Trace tool

Operator tools and lifecycle management

  • CLI with built-in help and validations
  • Installation using CLI
  • Zero downtime upgrade of RDI
  • Status tool for health and data provenance
  • Monitoring using Prometheus and Grafana