Start with the destination.
Configure source-to-consumer mappings and synchronization tasks around the data each downstream system needs.
Deltaplexvs.Confluent
Enterprise data delivery, built around your consumers.
Organize integration around sources, mappings and destinations. Connect Kafka where it serves your architecture.
Deltaplex vs. Confluent comparison
| What matters | Deltaplex | Confluent |
|---|---|---|
| Starting point | Sources, mappings and synchronization tasks | Kafka streams, connectors and processing services |
| Consumer delivery | Mapped flows to supported destinations | Topics, connectors and applications |
| Processing | Mapping, filtering and cleansing scripts | Connectors and stream processing, including Flink |
| Data checks | Supported-node count and content checks | Scope follows the connector-to-destination design |
Source, destination and version support varies by integration path.
Why Deltaplex
Built for your next move.
Prepare and distribute changes.
Apply mappings, filters and cleansing scripts; serve multiple consumers or consolidate sharded sources.
Verify the delivered result.
Use row-count and content checks on supported nodes alongside task monitoring and alerts.
See Deltaplex in action
Follow a change to its consumer.
See the mapping, delivery and data check together.
A few quick answers.
Can Deltaplex work with Kafka?
Yes. Deltaplex supports Kafka nodes and can feed or consume Kafka on supported paths, alongside other destinations. Keep event retention and consumer requirements in the architecture design.
What preparation can happen inside the flow?
Configure structure mappings, row and column filters, and per-mapping cleansing scripts. One-to-many mappings shape consumer-specific delivery; many-to-one mappings support consolidation of sharded sources.
Product names belong to their respective owners. Deltaplex is independent of the vendors compared.