pachyderm VS typhoon-orchestrator

20. Pachyderm | Github | tutorial

Pachyderm specializes in creating compliance-focused pipelines that integrate with enterprise-level storage solutions.

There are a couple of other contenders in this space. DVC (https://dvc.org/) seems most similar.

If you're interested in something you can self-host... I work on Pachyderm (https://github.com/pachyderm/pachyderm), which doesn't have a Git-like interface, but also implements data versioning. Our approach de-duplicates between files (even very small files), and our storage algorithm doesn't create objects proportional to O(n) directory nesting depth as Xet appears to. (Xet is very much like Git in that respect.)

The data versioning system enables us to run pipelines based on changes to your data; the pipelines declare what files they read, and that allows us to schedule processing jobs that only reprocess new or changed data, while still giving you a full view of what "would" have happened if all the data had been reprocessed. This, to me, is the key advantage of data versioning; you can save hundreds of thousands of dollars on compute. Being able to undo an oopsie is just icing on the cake.

Xet's system for mounting a remote repo as a filesystem is a good idea. We do that too :)

Pachyderm - License prevents competition.

I was at Airbnb when we open-sourced Airflow, it was a great solution to the problems we had at the time. It's amazing how many more use cases people have found for it since then. At the time it was pretty focused on solving our problem of orchestrating a largely static DAG of SQL jobs. It could do other stuff even then, but that was mostly what we were using it for. Airflow has become a victim of its success as it's expanded to meet every problem which could ever be considered a data workflow. The flaws and horror stories in the post and comments here definitely resonate with me. Around the time Airflow was opensource I starting working on data-centric approach to workflow management called Pachyderm[0]. By data-centric I mean that it's focused around the data itself, and its storage, versioning, orchestration and lineage. This leads to a system that feels radically different from a job focused system like Airflow. In a data-centric system your spaghetti nest of DAGs is greatly simplified as the data itself is used to describe most of the complexity. The benefit is that data is a lot simpler to reason about, it's not a living thing that needs to run in a certain way, it just exists, and because it's versioned you have strong guarantees about how it can change.

[0] https://github.com/pachyderm/pachyderm

Here is a demo run of lgtm on pachyderm

I have my own opinion on Airflow's pain points and created Typhoon Orchestrator (https://github.com/typhoon-data-org/typhoon-orchestrator) to solve them. It doesn't have many stars yet but I've used it to create some pipelines for medium sized companies in a few days, and they've been running for over a year without issues.

In particular I transpile to Airflow code (can also deploy to Lambda) because I think it's still the most robust and well supported "runtime", I just don't think the developer experience is that good.

Typhoon Orchestrator This is a framework that I designed to help fix some of the pain points of Airflow so that I could build test and deploy pipelines faster. You could skip this step but if you want more info check here.

I am also collaborating on an open source tool called Typhoon Orchestrator (repo). It aims to make composing airflow data pipelines simple and quite quick. Putting pipeline steps together like lego.

The project (https://github.com/typhoon-data-org/typhoon-orchestrator) doesn't have many stars yet but I have deployed it on a medium sized hotel chain for several data sources with a similar use case to yours and it's been working for over a year with no intervention. If you decide to pursue this option I'd be willing to provide provide some support free of charge (feel free to PM me).

Typhoon Orchestrator is a great way to deploy ETL workflow on AWS Lambda. In this tutorial we intend to show how easy to use and versatile it is by deploying code to Lambda that gets a random joke from https://jokeapi.dev once a day and sends it to your telegram group.

I think there's space for an open source library that can help with what you described. We originally created https://github.com/typhoon-data-org/typhoon-orchestrator to orchestrate ETL workflows, which would be a superset of the use cases you described. Our next goal is to allow deployment to AWS lambda which can be a good compromise between getting locked in with SAAS and hosting your own infrastructure.

Also check out Zappa's scheduled tasks that have a similar goal and inspired our library.