dbd VS tbls

I've created two examples of how to use the dbd tool to load Kaggle dataset data files (csv, json, xls, parquet) to your Postgres, MySQL, or SQLite database.Basically, you don't have to create any tables, nor run any SQL INSERT or COPY statements. Everything is automated. You just reference the datasets and files with a URL and execute a 'dbd run' command.The examples are here. Perhaps you find it useful. Let me know, what you think!

I've created two examples of how to use the [dbd](https://github.com/zsvoboda/dbd) tool to load Kaggle dataset data files (csv, json, xls, parquet) to your Postgres, MySQL, or SQLite database.

I work on the new open-sourced tool called dbd that enables you to load data from your local data files to your database and transform it using insert-from-select statements. The tool supports templating (Jinja2). It works with Postgres, MySQL, SQLite, Snowflake, Redshift, and BigQuery.

I was looking for some declarative ELT tool for creating my analytics solutions, and DBT was the closest I've found. I liked its concept, but I came across quite a few limitations when I wanted to use it. I couldn't specify and create basic things like data types, indexes, primary/foreign keys, etc. In the end, I decided to implement my own - more straightforward and more flexible. I've published the result - dbd on GitHub. Perhaps, you can find it helpful. Your feedback is greatly appreciated!

Autogenerated database documentation is often pretty hit and miss but tbls[1] does a pretty good job in that space. Especially when you comment on your tables, fields, views, functions etc (which is a good habit anyway!) the output is quite useful

[1] https://github.com/k1LoW/tbls

In-database comments combined with something like https://github.com/k1LoW/tbls make for very cheap database documentation.

No affiliation with tbls except that I'm a big fan

Check out tbls. You can create a ERD in one command