Redis Data Integration is a product for data ingestion from existing systems of record (mainly databases) into Redis Enterprise. You can think of Redis Data Integration as a streaming ELT process:

  • Data is Extracted from the source database using Debezium connectors.
  • Data is then Loaded into Redis Data Integration, a Redis database instance that keeps the data in Redis streams alongside required metadata.
  • Data is then Transformed using provided RedisGears recipes and written to the target Redis DB.

Redis Data Integration using Debezium works in 2 modes:

  1. Initial sync - Where a snapshot of the entire database or a subset of selected tables is used as a baseline. The entire dataset is streamed to Redis Data Integration and then transformed and written into the target Redis database.
  2. Live updates - Where Debezium captures changes to the data that happen after the baseline snapshot and streams them to Redis Data Integration where they are transformed and written to the target.

High-level Redis Data Integration architecture

Supported data transformations

Currently Redis Data Integration supports transforming data from RDBMS row into a Redis Hash. In the short term we will add support for RDBMS row into RedisJSON document. See the data types list for the exact mappings.

Architecture and components

Detailed Redis Data Integration architecture


Currently the only source for Redis Data Integration is the Debezium Redis Sink Connector. Other sources and SDK will be added in later versions.

Redis data integration data plane

RDI data streams

Redis Data Integration receives data using Redis streams. Records with data from a specific database table are written to a stream with a key reflecting the table name. This allows a simple interface into Redis Data Integration and keeping the order of changes as served by Debezium.

RDI engine

RDI Engine is built on top of RedisGears. It has 2 main logical functions:

  • Transformation of data - In the current version, we only support Debezium types into Redis types but future versions will support Apache Avro. In addition we plan to provide data mapping between denormalized relational model and denormalized Redis JSON documents.
  • Writing data - This is the part where Redis Data Integration writes the data to the target ensuring order & guarantying at least once to prevent data loss. In addition, the Writer can be configured to ignore, abort or keep rejected data entries.

Redis data integration control plane


Redis Data Integration comes with a python based CLI that is intuitive, self explanatory & validating to ease the entire lifecycle of Redis Data Integration setup and management.

RDI configuration

Redis Data Integration configuration is persisted at the cluster level. The configuration is written by the CLI deploy command, reflecting the configuration file. This mechanism allows for automatic configuration of new shards when needed and can survive shard and node failure.

Secrets handling

Redis Data Integration requires the following secrets:

  • The RDI Engine requires the credentials and certificate to connect and write encrypted data to the target Redis DB.
  • The Debezium Redis Sink Connector requires the credentials and certificate to connect and write data into Redis Data Integration DB.
  • The Debezium Source Connector requires the source DB secrets.
  • The RDI CLI requires a Certificate to connect to the Redis Enterprise cluster. For the create command it requires the credentials of a privileged user but it does not cache or store these credentials.

Credentials and Certificate can be served in one of the following ways:

  • Entered manually as parameters to a CLI command (where applicable).
  • Stored in environment variables.
  • Injected to the temp file system by a secret store agent (for example, HashiCorp Vault).

Scalability and high availability

Redis Data Integration is highly available:

  • At the feeder level: Debezium Server is deployed using Kubernetes or Pacemaker for failover between stateless instances, while the state is secured in Redis.
  • At the Data and Control Plane: Using Redis Enterprise mechanisms for high availability (shard replica, cluster level configurations, and so on)

Redis Data Integration is horizontally scalable:

  • Table level data streams can be split to multiple shards where they can be transformed and written to the target in parallel, while keeping table level order of updates.

Tested topologies

Redis Data Integration is running on Redis Enterprise. It works on any installation of Redis Enterprise regardless of the runtime (bare metal, VMs, or containers). Redis Data Integration can be collocated with the target Redis DB or run on a different Redis Enterprise cluster.

Debezium Server should run in warm High Availability topology in one of the following options:

  • Running on 2 VMs in an active/passive setup. The failover orchestration can be done by tools such as Pacemaker.
  • Running as a Kubernetes pod where Kubernetes watches the pod liveliness and readiness and acts to recover nonfunctional pod It is important to note that Redis Data Integration does not keep any state on Debezium. All state is kept in Redis Data Integration.