Frameworks for Python data apps
Streamlit • A faster way to build and share data apps
- Gradio or streamlit for prototyping and why? : r/LocalLLaMA
- Creating dashboards with Python? (Dash vs Flask + Plotly vs others) : r/datascience
Pretty plots: DIY / dev tooling
- ManimCommunity/manim: A community-maintained Python framework for creating mathematical animations.
- 3b1b/manim: Animation engine for explanatory math videos
Basic analytics tooling
- I trialled both Looker and Superset in a prior job. Looker was an easier out-of-the-box solution, and the team wasn’t psyched about self-hosting Superset: there was no long-term dedicated data team so the less to manage, the better. Generally, infra folks pitched in as needed; I was dedicated to data for a pipeline rewrite and monitoring set up, but that was temporary.
- Unfortunately, Looker was significantly less expressive than Superset, at least in the comparison I did using a test self-hosted version of Superset vs Looker circa 2022. I was ultimately able to transition the team off of Looker by using a hosted solution for Superset: Preset.
- Despite our data being stored/managed on GCP (using both storage buckets and Big Query), Preset offered more flexibility than Looker. That enabled a greater range of analysis that made it possible to set up not only customer service and business monitoring dashboards but also product dev and infrastructure monitoring. We also used both Honeycomb and Datadog for infrastructure monitoring, but replicating some of that functionality enabled a coordinated view in Preset on user, usage, and infrastructure data by all teams. A common perspective was really helpful both in getting a comprehensive lay of the land without clicking through multiple services and in enabling cross-correlation of data that wouldn’t otherwise land in the same service. Again, no dedicated data team so, though creating a unified view into multiple services would have been an option, we didn’t have the bandwidth.