The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. The Meltano project is aiming to provide a solution to that situation. In this episode, project lead Douwe Maan shares the history of how Meltano got started, the motivation for the recent shift in focus, and how it is implemented. The Singer ecosystem has laid the groundwork for a great option to empower teams of all sizes to unlock the value of their Data and Meltano is building the reamining structure to make it a fully featured contender for proprietary systems.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Douwe Maan about Meltano, an open source platform for building, running & orchestrating ELT pipelines.
- How did you get involved in the area of data management?
- Can you start by describing what Meltano is and the story behind it?
- Who is the target audience?
- How does the focus on small or early stage organizations constrain the architectural decisions that go into Meltano?
- What have you found to be the complexities in trying to encapsulate the entirety of the data lifecycle in a single tool or platform?
- What are the most painful transitions in that lifecycle and how does that pain manifest?
- How and why has the focus of the project shifted from its original vision?
- With your current focus on the data integration/data transfer stage of the lifecycle, what are you seeing as the biggest barriers to entry with the current ecosystem?
- What are the main elements of your strategy to address these barriers?
- How is the Meltano platform in its current incarnation implemented?
- How much of the original architecture have you been able to retain, and how have you evolved it to align with your new direction?
- What have you found to be the challenges that your users face when going from the easy on-ramp of local execution to then trying to scale and customize their pipelines for production use?
- What are the most critical features that you are focusing on building now to make Meltano competitive with managed platforms?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Meltano?
- When is Meltano the wrong choice?
- What is your broad vision for the future of Meltano?
- What are the most immediate needs for contribution that will help you realize that vision?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Mexico City
- Locally Optimistic
- Stitch Data
- Version Control
- Code Review
- Jupyter Notebook
- Meltano Modeling Syntax
- Apache Superset
- Apache Airflow
- 12 Factor Application