pachyderm VS kestra

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

Kestra's communication is asynchronous and based on a queuing mechanism. It leverages the Micronaut framework and offers two runners: one that uses a database (JDBC) for both the message queue and resource storage, and another that uses Kafka as the message queue and Elasticsearch as the resource storage. The platform is fully extensible and plugin-based, providing a rich set of plugins for various workflow tasks, triggers, and data storage options. For those interested, the GitHub repository is available here: https://github.com/kestra-io/kestra

For everyone wondering https://github.com/kestra-io/kestra/discussions/468

If you need any guidance with your Snowflake deployment, our experts at Kestra would love to hear from you. Let us know if you would like us to add more plugins to the list. Or start building your custom Kestra plugin today and send it our way. We always welcome contributions!

But I totally agree that a large static dag is not appropriate in the actual data world with data mesh and domain responsibility.

[0] https://github.com/kestra-io/kestra

- We have plans to add another option that will replace both dependencies with jdbc (https://github.com/kestra-io/kestra/pull/368), is theses dependencies more comfortable for you?

With Kestra's innate flexibility, and many integrations, you are not locked into the choice of one ingestion method or the other. Complex workflows can be developed, whether in parallel or sequentially, to deliver both ELT and ETL processes. Simple descriptive yaml is used to connect plugins, and to create flows. Because workflows created in Kestra are represented visually, and issues can be seen in relation to individual tasks, there is no need to fear complexity. Trouble can be traced to its source in an instant, allowing you to try new things and come up with a new solution without fear. Give it a try, and let us know what you come up with!

Kestra is an orchestration and scheduling platform that is designed to simplify the building, running, scheduling, and monitoring of complex data pipelines. Data pipelines can be built in real-time, no matter how complex the workflow, and can connect to multiple resources as needed (including Debezium).