Modern ETL Pipelines without the Complexity
Turn big data into insights throughout your organization with the power of Apache Spark on Databricks, AWS EMR, Google Cloud Dataproc, SQL Server 2019 Big Data Cluster, and other Spark clusters. StreamSets Transformer Engine is a data pipeline engine designed for any developer or data engineer (with or without Scala or Python skills) to build ETL and ML pipelines that execute on Apache Spark.
Run Apache Spark anywhere now and in the future as your needs evolve.
Operationalize Your Data Transformations
No Code ETL for Apache Spark
Put Apache Spark 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 DataOps Platform helps your team leverage Apache Spark to accelerate your data projects without deep Scala expertise. Advanced Spark developers can easily operationalize their code with custom processors and automate critical Spark operations.
Run on Multiple Spark Platforms
Transformer Engine is designed to run ETL operations on all major Spark distributions for maximum flexibility. You can natively execute on AWS EMR, HDInsight, and Databricks platforms, and in containerized Spark environments such as Microsoft SQL Server 2019 Big Data Cluster. Run your development and production projects on multiple Apache Spark platforms or support different business needs from a single tool.
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 Engine offers the enterprise features and agility of legacy ETL tools, while revealing the full power and opportunity of Apache Spark.