Apache Hop
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VisitApache Hop vs Bonobo
What both publish
| Fact | Apache Hop | Bonobo |
|---|---|---|
| Execution | The same pipeline can run locally, on Hop Server, on Apache Spark, or on Flink and Dataflow through Apache Beam. | Bonobo can execute graph nodes in parallel and show live job statistics through its command-line interface. |
| Integrations | Listed integrations include PostgreSQL, MySQL, Oracle, SQL Server, Snowflake, BigQuery, Redshift, Databricks, MongoDB, Neo4j, Kafka, Cassandra, Salesforce, SAP, Parquet, Avro, S3, Azure Blob, Google Cloud Storage, REST and SFTP. | The SQLAlchemy extension adds capabilities for extracting from and loading into SQL databases. |
| License | The project says Hop is developed in the open under the Apache License 2.0. | Bonobo and its extensions are released under the Apache License 2.0. |
| Purpose | Apache Hop is an open source platform for data integration and orchestration, with visual pipelines and workflows. | Bonobo is a lightweight Python ETL framework for building data transformation pipelines. |
Apache Hop plans
- Apache HopFree
Bonobo plans
- BonoboFree
Pick Apache Hop if…
Hop suits data teams that want visual pipeline design and workflow orchestration across varied databases and execution environments. It is appropriate for users able to manage Java 21 for version 2.19.0 and carefully restrict destructive operations.
Pick Bonobo if…
Bonobo suits Python developers building self-hosted ETL jobs with small, testable transformations. It is intended for small-scale data rather than setups needing a client-server component or daemon.