Centrally Manage Many-to-Many Dataflow Topologies

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Data Architect

It is challenging to build data movement architectures that bring together a variety of traditional and new data sources, and then keep it all running reliably as things inevitably change.

The StreamSets DataOps platform gives you the tools to design, deploy, scale and reliably operate modern data movement that encompasses numerous data sources supplying on-prem and cloud computing platforms.

Control the Dataflow Life Cycle

Our platform was designed based on the assumption that data movement is always evolving — to support new data sources, to take advantage of new and updated compute platforms and to accommodate changing analytics needs. With it, you can govern your data movement logic from test to stage to production, manage performance by setting Data SLAs, and compare performance across versions as you update your topologies.

Map and Measure Live Performance

You can’t manage what you can’t see. StreamSets Control Hub gives you a live data map based on metadata emitted by StreamSets Data Collectors. For the first time, you can visualize connections between sources, stores and other infrastructure. You can measure throughput, latency and errors across any path on the map, be it end to end, point to point, or simply into and out of an individual system.

Scale Up or Out

As data volumes grow, it is important that your dataflows don’t become bottlenecks as you funnel real-time data to important applications. StreamSets Data Collector, our data movement execution engine, runs natively and automatically on YARN or Mesos clusters or within Kubernetes environments. It also natively supports multithreaded operation and high availability.

Collaboration via a Shared Pipeline Repository

Data movement pipelines are being built by developers and data scientists across your business. StreamSets helps you centralize the design of dataflow logic in order to facilitate collaboration, reuse and establishment of best practices and corporate standards. It includes a pipeline repository and version control over dataflows to support a disciplined production process.

WHITE PAPER
6 Simple Steps for Replatforming in the Age of the Data Lake

Let your data flow

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