telepresence VS Kedro

I trialled Telepresence[0] for my company 2 or 3 years ago, that does this sort of thing very slickly. It didn't quite work for us back then, I forget why, but I imagine it's come along a way since then.

[0] https://www.telepresence.io

How does this compare to Telepresence?

seems to be very similar to https://www.telepresence.io

If they're web services you work on, you might try https://www.telepresence.io/ (Requires something to be installed in the cluster though, easily done).

Depending on the context, the type of code you do, you may want to also look into the sister protocol to LSP, DAP—debug adaptor protocol. It really depends on your context whether local dev, dev against a remote server, and if the latter whether you run under GCP and thus have the “Snapshot Debugger”, or under Kubernetes with something like Ambassadar/Emissary and thus can run Telepresence, whether you do local or remote Docker and thus most IDEs don't necessarily magically work especially if the containers are competently locked down, etc.

If you're on Kubernetes remotely, Telepresence [0] might be worth a look.

[0] https://www.telepresence.io

Sounds Like Telepresence (https://github.com/telepresenceio/telepresence) which intercepts traffic to a service on the cluster and directs it to your local environment.

This seems to be very similar to Telepresence, which I just couldn't get to work for us.

What the difference with okteto and telepresence ?

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.