A majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future.
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- Your host as usual is Tobias Macey and today I’m interviewing Romain Dorgueil about Bonobo, a data processing toolkit for modern Python
- How did you get introduced to Python?
- What is Bonobo and what was your motivation for creating it?
- What is the story behind the name?
- How does Bonobo differ from projects such as Luigi or Airflow?
[RD] After I explain why that’s totally different things, maybe a good follow up would be to ask about differences from other data streaming solutions, like Apache Beam or Spark.
- How is Bonobo implemented and how has its architecture evolved since you began working on it?
- What have been some of the most challenging aspects of building and maintaining Bonobo?
- What are some extensions that you would like to have but don’t have the time to implement?
- What are some of the most interesting or creative uses of Bonobo that you are aware of?
- What do you have planned for the future of Bonobo?
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