dbt-coves VS dbd

This just feels like you aren’t using the plentiful tools to make those “mind-numbingly slow” dev steps faster. For ex., using dbt-coves to generate the staging models with casting to types in a couple clicks. And pulling directly from Fivetran tables is just poor practice, with the additional steps needed to do it “right” being inconsequential at best.

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!