Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression. Learn more →
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Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
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Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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pachyderm reviews and mentions
Show HN: We scaled Git to support 1 TB repos
9 projects | news.ycombinator.com | 13 Dec 2022
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 :)
Awesome list of VCs investing in commercial open-source startups
6 projects | /r/opensource | 14 Sep 2022
Pachyderm - License prevents competition.
6 projects | news.ycombinator.com | 2 Aug 2022
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. 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.
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6 projects | dev.to | 7 Mar 2022
Here is a demo run of lgtm on pachyderm
Dud: a tool for versioning data alongside source code, written in Go
2 projects | /r/golang | 21 Jun 2021
A note from our sponsor - InfluxDB
www.influxdata.com | 4 Jun 2023
pachyderm/pachyderm is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of pachyderm is Go.