sqlitestudio VS Kedro

DB4S provides only one algorithm based on official SQLite cipher. You can encrypt your database with another in SQLiteStudio or sqlite-gui (I'm an author). Both applications use SQLite3 Multiple Ciphers-library.

SQLiteStudio is my favourite - https://sqlitestudio.pl/

The most popular apps areDB4S and SQLiteStudio. If you are planning to run long time queries, then you might encounter with problems by running them in parallel in these tools. To run several queries in a real parallel mode you can use Navicat for SQLite or my sqlite-gui.

If you are up to learn SQL queries, I would suggest using SQLite. It stores all information in a single file. You even can use https://sqlitestudio.pl solution so it can be a little more visual.

DB Browser for SQLite and SQLiteStudio both exists, not sure if this is what you are looking for.

Perhaps the simplest, for your case, would be a tool made specifically for SQLite, such as SQLite Studio.

They're database files that you can inspect with a program like DB Browser or SQLiteStudio. You should be able to import permissions.sqlite to different Firefox profiles by just copying it to the right profile directory.

Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).

# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]

So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.

We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.

you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data

Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.

You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.

An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.