What is Apache Hop?

Hop allows data professionals to work visually, using metadata to describe how data should be processed. Visual design enables data developers to focus on what they want to do instead of how that task needs to be done. This focus on the task at hand lets Hop developers be more productive than they would be when writing code.

Hop was designed to be as flexible as possible: at the core is the small but powerful Hop engine. All functionality is added through plugins: the default Hop installation comes with about 400 plugins. You can remove or add third-party plugins according to your needs to tailor Hop to be exactly what you need. Hop is designed to work in any scenario, from IoT to huge volumes of data, on-prem, in the cloud, on a bare OS or in containers and kubernetes.

In workflows and pipelines, hundreds of operations can be applied on the data: read from and write to a variety of source and target platforms, but also combine, enrich, clean and in many other ways manipulate data. Depending on the engine and selected functionality, your data can be processed in batch, streaming or in a batch/streaming hybrid.

A number of common use cases for Hop are:

• Integrate between diverse data architectures, combining relational databases, files, NoSQL databases like Neo4j, MongoDB, Cassandra etc