The Fast Track to Kafka Success

Build pipelines quickly with a drag-and-drop interface, run pipelines reliably with live data metrics, and stay agile when requirements change.

Tame Kafka with StreamSets

Apache Kafka is a scalable, fault tolerant modern messaging system common in publish and subscribe (pub/sub) architectures. Kafka is used for a range of use cases including message bus modernization, microservices architectures and ETL over streaming data.

Open source StreamSets Data Collector, with over 2 million downloads, provides an IDE for building pipelines that include drag-and-drop Kafka Producers and Consumers. StreamSets Control Hub lets you build and manage many-to-many data movement topologies that include Kafka plus other popular storage/compute platforms.

With StreamSets, you can build pipelines faster, deploy and update them with discipline, monitor live end-to-end data performance (beyond the Kafka cluster), and protect data privacy with rules-based in-stream handling of sensitive data.

Challenges with Kafka

Developer Skills Gap

Getting skilled up with Kafka takes time and effort. Lack of available skills can lead to project delays and even failure.

Limited Connectivity

Connectivity between sources and destinations through Kafka Connect is limited and, where it does exist, is often community-driven and lacks commercial support.

Endless Maintenance

Kafka project requirements can be fluid during initial design as well as after entering production. Continually coding changes in Kafka Connect needlessly ties developers to low-value maintenance.

Poor Data Monitoring

Kafka lacks native visibility into the health of data-in-motion, meaning you miss problems that impact data availity, integrity and privacy.

StreamSets + Kafka = Productivity + Visibility + Agility

Pipelines in Minutes

    • Drag and drop Kafka Producers and Consumers to connect your cluster to any source or destination.
    • Design, preview, test and run pipelines continuously using our full-featured IDE.
    • Transform data in stream with built in processors or invoke custom code.

Reliable Continuous Operation

  • Continuously monitor data characteristics from source to destination.
  • Set Data SLAs around availability, quality and security.
  • Get pipeline failover for greater resilience.
  • Detect and protect sensitive data in-stream.

Agility in the Face of Change

    • Automatic handling of schematic and semantic data changes.
    • Snapshots and data ingestion for quick problem diagnosis.
    • Dynamic and configurable topic and partition management.
Stream into Kafka
How to Build a Kafka Pipeline

Let your data flow

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