While it's extensible, the primary use case for Steampipe so far is to query DevOps data (e.g. AWS, GitHub, Kubernetes, Terraform files, etc) - so often they ar

While it's extensible, the primary use case for Steampipe so far is to query DevOps data (e.g. AWS, GitHub, Kubernetes, Terraform files, etc) - so often they are working within the Steampipe DB itself and combining with CSV data etc. But, because it's just Postgres, it can be integrated into many different data infrastructure strategies. Many users query Steampipe from standard BI tools, others use it to extract data into S3, it has also been connected with BigQuery - https://briansuk.medium.com/connecting-steampipe-with-google-bigquery-ae37f258090f https://briansuk.medium.com/connecting-steampipe-with-google... As opposed to a lake or a warehouse, we think of it as a "Data Rainbow" - structured, ephemeral queries on live API data. Because it doesn't have to import the data it works uniquely well for small, wide data and joining large data sets (e.g. search, querying logs). I spoke about this in detail at the Yow Data conference - https://www.youtube.com/watch?v=2BNzIU5SFaw https://www.youtube.com/watch?v=2BNzIU5SFaw