boxball VS condenser

I haven't worked with BI tools, but I'll make the same recommendation for Retrosheet data that I always do: check out the Boxball distributions, specifically the CSV files (or the parquet files if you prefer). You can load those right in, rather than futzing with Retrosheet's event files and processing them manually, etc., just to get to the same spot.

The original data source is the Baseball Databank (aka "The Lahman Database"). I use the Boxball distributions, which include both retrosheet and Baseball Databank data.

You know, I thought about that after posting, that the dailies might not be available directly from retrosheet. I use the Boxball distributions, which take the retrosheet data and process it into database images. These distributions include a "daily" table with the daily logs. So apparently Boxball is generating that table.

I used to think the same thing, and then I found the Boxball pre-built images. You still have to be a bit handy to get them working, but it's way easier than starting from scratch with raw retrosheet data. I initially used the postgres image and worked in SQL, and then switched to python/pandas and the parquet files.

I've always been good with data, math, Excel, etc., but decided to take it to another level during the pandemic. The most difficult thing about Retrosheet data is getting into a usable format. Luckily, the boxball project has created a number of ready-to-download images with all of the data, for different database technologies.

For Retrosheet data, I've been using pre-built images from Boxball, rather than going through the whole process of downloading and converting the files from retrosheet. The Boxball images are awesome. I mostly use the Parquet files for Pandas, but they have other formats (e.g., postgres Docker images, CSV, etc) there.

[1]: https://github.com/TonicAI/condenser

[1]: https://github.com/TonicAI/condenser

This may be overkill but we have used Tonic for this: https://www.tonic.ai/

A tool like https://www.tonic.ai/ might help.

I attended a presentation a month or so ago where a co-worker was advocating for tonic. I've never used it myself, but, definitely looks as though it would cover your bases. I do agree with onomazein though that this should really be handled on the infrastructure side if at all possible. Dev's, testers, anyone else should be able to pull from an already anonymized location, but, but, someone else should be responsible for setting up the environment and the initial synchronization.

So I built a gitlab pipeline to create a backup of this upstream db without downtime using various SQL utils. This archive is then staged into an image so the data will unpack and load on startup. I then used a data subsetter called condenser to create datasets for certain use cases. Now devs can load reliable dev data quicker, test against data that QA uses but within their unique envs (local and preview) and create datasets for their own use cases.