Earlier quoted context omitted.
We orchestrate our ETL pipelines with dagster. We only use duckdb in a few of them but are slowly replacing pandas etls with it. For some of our bigger jobs we use spark instead. Essentially it's: 1. Data sources from places such as s3, sftp, rds 2. Use duckdb to load most of these with only extensions (I dont believe there's one for sftp, so we just have some python code to pull the files out.) 3. transform the data…
This is very cool!. Last time I checked duckdb didn't have the concept of a metastore so do you have an internal convention for table locations and folder structure ?. What do you use for reports/visualizations? notebooks ?.
Reports and viz varies a lot, the finance department uses tableau where as for more 'data sciencey' stuff we normally just use notebooks.