ETL on Hadoop Built for Today’s Big Data Needs

Hydrograph, a next-generation data integration tool, accelerates ETL development in the big data ecosystem. Designed in partnership with business users, Hydrograph addresses a need for ETL functionality for Hadoop and Spark in enterprises with big data workloads.

With its drag-and-drop GUI environment, Hydrograph provides an easy-to-use tool for developing ETLs on open source big data platforms without a need for writing MapReduce code or knowledge of Hadoop or Spark.


ETL on Hadoop Built for Today’s Big Data Needs

Full Feature ETL Solution for Big Data

Hydrograph provides ETL (extract, transform and load) functionality on Hadoop and Spark in enterprises with big data workloads without the need to write MapReduce code.

ETL Offload to Hadoop and Spark

Hydrograph can cut costs and increase computation power by re-using ETL processes of your existing ETL applications on Hadoop and Spark.

Power the Data Lake

Hydrograph enables you to effortlessly build new ETL processes on data in your Hadoop data lake.

Hydrograph Architecture

Hydrograph Architecture

Hydrograph Advantage

  • Increases ETL Developer productivity on Hadoop by up to 50%
  • Ports existing ETL processes to Hadoop with little or no change
  • Views ETL processes in real-time for service level management
  • Cloud Agnostic: Same data flow can run on any cloud service (EMR, GCP, Azure) as well as in-house clusters and local machines
  • Distribution Agnostic: Same data flow can run on leading Hadoop distributions like HDP, CDH, MapR and any big data appliances


Hydrograph ushers in a new era of Hadoop and Spark adoption for enterprises that are implementing an open source big data strategy by facilitating the migration of complex data integration jobs to more flexible and future-proof environments

Open Source


Now Available on GitHub


From the Bit Blog

Traditional ETL vs ELT on Hadoop

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The advent of Hadoop has taken enterprises by storm. The majority of enterprises today have one or more Hadoop cluster at various stages of maturity within their organization. Enterprises are trying to cut down on infrastructure and licensing costs by offloading storage and processing to Hadoop.