Where DevOps Meets Data Integration

Efficiency.   Agility.   Reliability.   Confidence.

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Modern Data Movement

From Edge to Data Center to Cloud

Build Batch & Streaming Pipelines in Hours

Drag-and-drop UI for connecting sources to destinations with transformations. Build, preview, debug and schedule from a single interface.

Execute at Enterprise Scale

Deploy in your data center or on AWS, Azure or GCP. Scale via YARN, Mesos or Kubernetes. Even execute on edge devices or servers.

Map and Monitor Runtime Performance

Track throughput, latency, error rates and more. Enforce Data SLAs. Drill into live map to pinpoint problems.

Protect Sensitive Data as it Arrives

Set policies to detect PII in-stream via pattern matching.  Hide, hash or quarantine data for GDPR and HIPAA compliance.

 

Trusted by enterprises worldwide

  • GSK
  • Cox Automotive
  • Availity
  • Ring Central
  • Vodafone

Accelerate Your Big Data Initiatives

100+ Ready-to-Use Origins and Destinations

No coding required

The DataOps Architectural Advantage

The StreamSets architecture is based on DevOps principles of automation and monitoring.

It helps enterprises build and maintain pipelines with much less effort, and keep pipelines running smoothly in the face of change.

Developer Productivity
  • Design “no code” pipeline templates.
  • Leverage pre-built origins, destinations and transformations.
  • Auto-generate new instances and schedule jobs.
  • Reuse best practice pipeline logic.
Operational Efficiency
  • Dataflow sensors monitor data and pipeline performance.
  • Visualize interconnected pipelines on topology map with drill down.
  • Set Data SLAs plus automated actions and alerts.
Architectural Agility
  • Add and upgrade data systems without pipeline downtime.
  • Detect data drift and auto-sync changed fields to downstream platforms.
  • Compare pipeline performance across versions.
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WHITE PAPER

DataOps DevOps for
Data Integration

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eBOOK

Using StreamSets to
Tame Apache Kafka

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ON-DEMAND WEBINAR

Hadoop Creator
Doug Cutting
Operationalizing Machine Learning and Advanced Analytics

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