Apache Hop vs Bonobo

Apache Hop is an open-source platform for data integration and orchestration. Its visual canvas lets users assemble pipelines, preview rows at individual steps, and watch live data and metrics during execution. A pipeline can run locally, on Hop Server, on Apache Spark, or on Flink and Dataflow through Apache Beam. Hop includes more than 250 pipeline transforms, more than 80 workflow actions, and support for over 40 database dialects. Listed connections include PostgreSQL, MySQL, Oracle, SQL Server, Snowflake, BigQuery, Kafka, Salesforce, SAP, S3, REST, and SFTP. Teams can switch environments when moving projects from development to production and keep credentials outside pipeline definitions. Apache Hop is free under the Apache License 2.0. It runs on Linux, macOS, and Windows; Hop Web works in modern browsers. Release 2.19.0 requires Java 21. Docker images and a Kubernetes Helm chart are available; Hop Web is work in progress and provided as-is.

Bonobo is a free, open-source ETL framework for Python developers building data transformation pipelines. It connects callable or iterable Python objects into directed graphs that it can visualize and execute. Data moves as streams of independent rows, with each node handling one row at a time while nodes can run in parallel. Transformations may be plain Python or transformation classes, organized as small units intended to support testing and maintenance. Built-in extractors and writers cover CSV, JSON, XML and XLS; the framework can also work with sources such as HTTP REST or SOAP APIs, filesystems, storage services, and SQL or NoSQL databases. The SQLAlchemy extension adds SQL database capabilities, while a Docker extension can run a local job with the bonobo runc command. Bonobo can also show live job status in a Jupyter Notebook. It is self-hosted, released under Apache License 2.0, and documented for small-scale data use. The documentation identifies it as alpha, so APIs may change.