In contrast my target audience is backend developers and hands-on engineering managers who want to build operational and business dashboards and recurring email exports by combining data from multiple different data sources. In both cases the audience for most traditional notebooks are data scientists. Thanks for asking! I hadn't seen it before but it looks pretty similar to Jupyter. Unless you are doing anything funky, all of these tools and tens of others will do the job. With all of this said, building simple reports and dashboards over aggregated data is a fairly commodity task nowadays. Tableau, Looker et al don't seem to be bring much to the table above these, and they are both quite difficult and expensive commercial organisations to deal with. I would look at both Metabase and Superset before the heavier weight commercial options if the choice was purely down to product features. I use a single binary vs (I think) a docker compose setup for Superset.īoth have a cloud offering, Preset.io is the Superset one which we are just using now and having a productive time with. It's also slightly more intimidating for end users too. I often find myself checking log files to see what actual SQL was issued from the front-end. It does however have a few extra rough edges and quirks where your query doesn't render as you would expect, especially with niche databases such as Druid and Clickhouse (via a third party driver). Superset is a great product and we are lucky to have this quality of BI tools for free. Simple to deploy and use, stable, easy for non techies and SQL analyst types. We ( ) use all of Metabase, Superset and heavier alternatives such as Tableau to build fairly advanced dashboards for customers.
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