The reason for collecting, cleaning, and organizing data is to make it usable by the organization. One of the most common and widely used methods of access is through a business intelligence dashboard. Superset is an open source option that has been gaining popularity due to its flexibility and extensible feature set. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. He digs into how it integrates with your data stack, how you can extend it to fit your use case, and why open source systems are a good choice for your business intelligence. If you haven’t already tried out Superset then this conversation is well worth your time. Give it a listen and then take it for a test drive today.
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 Max Beauchemin about Superset, an open source platform for data exploration, dashboards, and business intelligence
How did you get involved in the area of data management?
Can you start by describing what Superset is?
Superset is becoming part of the reference architecture for a modern data stack. What are the factors that have contributed to its popularity over other tools such as Redash, Metabase, Looker, etc.?
Where do dashboarding and exploration tools like Superset fit in the responsibilities and workflow of a data engineer?
What are some of the challenges that Superset faces in being performant when working with large data sources?
Which data sources have you found to be the most challenging to work with?
What are some anti-patterns that users of Superset might run into when building out a dashboard?
What are some of the ways that users can surface data quality indicators (e.g. freshness, lineage, check results, etc.) in a Superset dashboard?
Another trend in analytics and dashboard tools is providing actionable insights. How can Superset support those use cases where a business user or analyst wants to perform an action based on the data that they are being shown?
How can Superset factor into a data governance strategy for the business?
What are some of the most interesting, innovative, or unexpected ways that you have seen Superset used?
What are the most interesting, unexpected, or challenging lessons that you have learned from working on Superset and founding Preset?
When is Superset the wrong choice?
What do you have planned for the future of Superset and Preset?
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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