Any-to-Any Dataflow Pipelines
in 1/10th the Time

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Data Engineer

If you’re struggling to flow batch and streaming data into big data stores and streaming platforms, StreamSets gives you drag-and-drop tooling that cuts your development time from months to days, while giving you the flexibility to run custom code where needed.

StreamSets Data Collector lets you create point-to-point pipelines while StreamSets Control Hub lets you design dataflow topologies comprising multiple pipelines in a collaborative cloud-native environment.

Design, Execute and Iterate in One Environment

StreamSets supports your DataOps practice with a single place to design, operate and iterate your pipelines. You design pipelines using a drag-and-drop GUI as part of a full-featured integrated development environment. You can design, preview, test and run your pipelines within the same interface, and get live metrics on dataflow performance.

Minimal Schema Means Higher Productivity

With StreamSets, you only specify the fields you’d like to act on, meaning you can create pipelines much more quickly and these pipelines are much more resistant to source schema changing (data drift).

Flexibility to Support All Kinds of Data

StreamSets lets you connect sources of batch and streaming, structured and unstructured data with a variety of big data platforms including Hadoop, Kalfa, Elastic, NoSQL and many more.

Automatic Data Drift Handling

Changes to schema and semantics without notice, known as data drift, can break pipelines and pollute data. StreamSets’ Intelligent Pipelines uniquely inspect the data to detect these changes, which can trigger alerts or actions that adjust downstream systems, such as adding a field to a Hive metastore.

12 Best Practices for Modern Data Ingestion

Let your data flow

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