Redis Data Integration
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.
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.
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:
|Oracle||12c, 19c, 21c|
|MongoDB||4.2, 4.4, 5.0, 6.0||Driver: 4.7|
|Percona XtraDB||5.7, 8.0.x|
|Postgres||10, 11, 12, 13, 14, 15|
|SQL Server||2017, 2019|
|Datastax Cassandra||>= 6.8.0|
Supported targets (write-behind)
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