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Apache Spark Transformer

Configure and manage your ETL pipelines on Spark without hand coding.

Modern ETL Pipelines without the Complexity

Turn unlimited data into insights in minutes with StreamSets Transformer for Spark. StreamSets Transformer runs on any Apache Spark environment (Databricks, AWS EMR, Google Cloud Dataproc, and Yarn) on premises and across clouds. StreamSets Transformer for Spark  is a data pipeline engine designed for any developer or data engineer to build and manage  ETL and ML pipelines that execute on Spark.

Create pipelines for performing ETL and machine learning operations using an intent-driven visual design tool

Troubleshoot with unparalleled visibility into the execution of Spark applications

Run any major Spark distribution and switch platforms without redesign

Runs On

Run Apache Spark anywhere now and in the future as your needs evolve.

Hadoop HDFS Apache Spark for ETL processing
MapR Apache Spark for ETL processing
Microsoft SQL Server Big Data Clusters Apache Spark for ETL processing
StreamSets for Databricks

Operationalize Your Data Transformations

Simplify Apache Spark for ETL for everyone

Build and Manage ETL and ML Pipelines That Execute on Spark

Put powerful and native ETL at the fingertips of any data engineer. Use a simple, drag-and-drop UI to create highly instrumented pipelines for performing ETL, stream processing, and machine learning operations. StreamSets Platform helps your team accelerate your data projects. Easily operationalize code and automate critical Spark operations through a central platform.

Unlock the Power of Apache Spark for Everyone

Run on Multiple Spark Platforms

Transformer Engines are designed to run on all major Spark distributions for maximum flexibility. You can natively execute on EMR, HDInsight, and Databricks platforms.  Run your development and production projects on multiple Spark platforms or support different business unit needs from a single tool without rework.  

Download: Design Considerations for Apache Spark
Visibility into Apache Spark Executions
Adopt Apache Spark for ETL and machine learning

See What Changed and Respond Easily

Full visibility and unmatched resiliency in your pipelines means you can stop hunting through log files for errors when change happens. Transformer pipelines are instrumented to provide deep visibility into Spark execution so you can troubleshoot at the pipeline level and at each stage in the pipeline. Transformer offers the enterprise features and productivity of legacy ETL tools, while revealing the full power and flexibility of Apache Spark.

Watch Demo: Changing Dimensions and StreamSets Transformer

The StreamSets Data Integration Platform

Build smart data pipelines in minutes and deploy across hybrid and multi-cloud platforms from a single log in.

Data Engineering for DataOps on AWS
Data Engineering for DataOps on Azure
Data Engineering for DataOps on Google Cloud
Data Engineering for DataOps on Snowflake
Data Engineering for DataOps on Databricks

Awards and Recognition

Top 50 IT Infrastructure Products G2 Badge

Data Engineers Gain Efficiencies With StreamSets


"The best feature of StreamSets is its intuitive visual interface, allowing us to effortlessly design, monitor, and manage data pipelines without the need for complex coding. This has significantly reduced our development time and made the process highly accessible to both technical and non-technical team members."

See full Review on G2

Mili M., Senior System Analyst
Mid-Market, (51-1000 emp.)


"StreamSets has lot of out of box features to use for data pipelines and connect AWS Kinesis, DB or Kafka and send to HDFS & Hive."

Read full review on G2

Sanath V.
Enterprise (> 1000 emp.)

Frequently Asked Questions

What is StreamSets Transformer for Spark?

  • Transformer for Spark allows users to create low to no code data pipelines that natively execute on Spark. Supported environments include Databricks, EMR, and HDInsight.
  • What is a StreamSets Transformer for Spark pipeline?

    All data pipelines for all of our engines, including Transformer for Spark, are essentially data flows. Taking data from one source to another and often including transformations along the way. Data pipelines can be leveraged to power machine learning, advanced analytics, business intelligence and other key insights.

    Is StreamSets an ETL tool?

    StreamSets acts as an ETL tool, though it is a complete end-to-end data integration platform. It performs ETL, ELT and data transformations such as joins, aggregates, and unions directly on Apache Spark and Snowflake platforms.

    How do I create an ETL pipeline in Spark with StreamSets?

    Transformer for Spark, StreamSets’ Spark Engine, acts as a Spark client that launches distributed Spark applications. Transformer passes the pipeline definitions and Spark runs it just as it would any other application, distributing the processing across nodes in the cluster. You can find more information on how to get started in the StreamSets documentation for Transformer.

    Can I still run Python code on Spark with StreamSets?

    Yes. StreamSets Transformer runs on any Apache Spark environment (Databricks, AWS EMR, Google Cloud Dataproc, and Yarn) on premises and across clouds. StreamSets Transformer for Spark is a data pipeline engine designed for any developer or data engineer to build and manage ETL and ML pipelines that execute on Spark.

    How do I install StreamSets Transformer for Spark?

    Installation information can be found in the Transformer Spark documentation..

    Helpful Resources
    Whitepapers & Ebooks

    The Hidden Problem Holding Back Finance Teams: Data Integration Friction


    Slowly Changing Dimensions & StreamSets Transformer

    Whitepapers & Ebooks

    Data Engineer’s Handbook: 4 Cloud Design Patterns

    4 Cloud Design Patterns for Data Ingestion and Transformation
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