Kedro VS projects

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.

You can look at the files in detail here. For this tutorial, I'll quickly mention a few crucial details.

Ploomber is the complete solution for notebook execution. It builds on top of papermill and extends it to allow writing multi-stage workflows where each task is a notebook. Meanwhile, it automatically manages orchestration. Hence you can run notebooks in parallel without having to write extra code.

The answer is YES, you can take advantage of OOP best practices to write good ETLs. For instance in this Ploomber sample ETL You can see there's a mix of .sql and .py files, it's within modular components so it's easier to test, deploy and execute. It's way easier than airflow since there's no infra work involved, you only have to setup your pipeline.yaml file. This also allows you to make the code WAY more maintainable and scalable, avoid redundant code and deploy faster :)

We have tons of examples that follow a standard layout, here’s one: https://github.com/ploomber/projects/tree/master/templates/ml-intermediate

I can talk about the open-source I'm working on Ploomber (https://github.com/ploomber/ploomber), it's focusing on seamless integration with Jupyter and IDEs. It allows an easy mechanism to orchestrate work for instance, here's an example SQL ETL and then you can deploy it anywhere, so if you're working with Airflow, it'll deploy it there too but without the complexity. You wouldn't have to maintain docker images etc.

I recommend using Ploomber which can help you build once and automate a lot of the work, and it works with python natively. It's open source so you can start with one of the examples, like the ML-basic example or the ETL one. It'll allow you to define the pipeline and then easily explain the flow with the DAG plot. Feel free to ask questions, I'm happy to help (I've built 100s of data pipelines over the years).

I'm not sure what pipeline frameworks support this kind of testing, but after successfully implementing this workflow, I added this feature to Ploomber, the project I'm working on. Here's how a pipeline looks like, and here's a tutorial.

Check out Ploomber, (disclaimer: I'm the author) it has a simple API, and you can export to Airflow, AWS, Kubernetes. Supports all databases that work with Python and you can seamlessly transfer from a SQL step to a Python step. Here's an example.

Without more specifics about your use case, it's hard to give more specific advice. But check out Ploomber (disclaimer: I'm the creator) - here's an example ETL pipeline. I've used it in past projects to develop Oracle ETL pipelines. Modularizing the analysis in many parts helps a lot with maintenance.

Yes! (tell your friend). You can write shell scripts so you can execute that 2002 code :) You can test it locally and then run it in AWS Batch/Argo. Here's an example