It's still early in the space, but there are real advantages to defining visualisations, metrics, and reports in a code-first way. Aside from being able to do more powerful things with something like pandas as opposed to Tableau, being able to check your dashboards into git, create reusable libraries internally, review changes in PR format etc., goes a long way to making things reproducible and less of a black hole. Also, creating reports and dashboards programmatically opens up some interesting use-cases where a drag and drop tool would break down.
We're building an open-source framework for creating reports and dashboards using Python, which you might find helpful: https://github.com/datapane/datapane. You can think of Datapane as the view layer / interface for any BI analysis you're doing using the open-source Python ecosystem. Any feedback would be much appreciated!
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It's still early in the space, but there are real advantages to defining visualisations, metrics, and reports in a code-first way. Aside from being able to do more powerful things with something like pandas as opposed to Tableau, being able to check your dashboards into git, create reusable libraries internally, review changes in PR format etc., goes a long way to making things reproducible and less of a black hole. Also, creating reports and dashboards programmatically opens up some interesting use-cases where a drag and drop tool would break down.
We're building an open-source framework for creating reports and dashboards using Python, which you might find helpful: https://github.com/datapane/datapane. You can think of Datapane as the view layer / interface for any BI analysis you're doing using the open-source Python ecosystem. Any feedback would be much appreciated!