The power of Apache Spark

minus the complexity.

Next generation ETL on Spark. Batch and streaming data processing in a single solution

StreamSets Transformer

StreamSets Transformer is an execution engine within the StreamSets DataOps platform that allows any user to create data processing pipelines that execute on Spark. StreamSets Transformer enables users to solve their core business problems without a deep technical understanding of Apache Spark.

Common Roadblocks for Data Engineering with Apache Spark

Specialized Skills

Taking advantage of engines like Apache Spark has too often been relegated to those with mature Scala/Java expertise.

Operational Blindness

Poor visibility into operation and performance leaves already taxed data teams searching for answers.

Fragmented Tooling

Separate tools for batch and stream processing create operational overhead and redundant data.

Download StreamSets Transformer

Get Started and Download a Trial StreamSets Transformer

No Hand Coding Needed

  • Bring the power and scale of Apache Spark to every developer.
  • Easy-to-use interface and rich tools democratize the process of data transformation.
  • Advanced users can use Scala and PySpark processors to extend the functionality of the platform.
  • Execute on any Spark Cluster, on-prem on Hadoop or on cloud hosted Spark Services.

Achieve Continuous Data and Monitoring.

  • No need to master batch or streaming semantics, the system handles any of them, mixing and matching as required.
  • Unparalleled visibility into Spark application execution.
  • Progressive error handling finds exactly where and why errors occur, without users needing to decipher complex log files.
  • Highly instrumented pipelines reveal exactly how every operation, and the application as a whole, is performing.
StreamSets Product Platform

Deeply Integrated with the StreamSets DataOps Platform

  • Unified platform for all patterns (ETL, streaming, edge)
  • Hosted pipeline designer in the cloud
  • Version control for pipelines
  • High level metrics and job monitoring
  • End-to-end visibility with topology view.
6 Steps for Replatforming
to a Data Lake

Let your data flow

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