The Apache Hop team just released Apache Hop 2.0.0, the first major release after graduating as a...

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We build and customize plugins for you, even entirely customized Hop builds are an option.
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Apache Hop is a perfect choice for all your data integration and data orchestration needs, but especially excels in a number of architectures and use cases.
Graphs are taking the world by storm with market leader Neo4j at the forefront. Graph data models and queries are extremely powerful and open up entirely new worlds of analyzing your data. However, reliable, repeatable, and scalable graph data loading often is not a trivial task.
Apache Hop's unparalleled graph data loading and querying functionalities put Hop in pole position for all your graph data loading needs.
Large-scale distributed data processing platforms like Apache Spark, Apache Flink, and Google Dataflow make processing huge amounts of data possible. Apache Beam offers a unified programming model to define ETL, batch and stream pipelines on any of these platforms.
Where Apache Beam makes creating a unified code base on all major distributed platforms possible, Apache Hop makes it easy. Hop's visual pipeline development, unit testing and project life cycle support allow data project teams to write pipelines once and run them where it makes most sense.
Apache Hop started its journey in 2019 as a fork of Kettle, the open source project behind Pentaho Data Integration.
Apache Hop and Kettle are different, independent and incompatible projects, this shared history allows Hop to import Kettle projects.
This upgrade not only converts Kettle jobs and transformations to Hop workflows and pipelines, but also gives access to
Check our blog for the latest news in Apache Hop, release previews and behind-the-scenes information
The Apache Hop team just released Apache Hop 2.0.0, the first major release after graduating as a...
The Apache Hop community PMC and community recently released Apache Hop 1.2.0. This release...
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