datastation-documentation VS juicefs

Hey folks! I quit my job at Oracle almost a year ago now to build DataStation. It's an app I've wanted as an engineering manager for years now.

DataStation helps you query a variety of data sources (conventional SQL like PostgreSQL and MySQL, non-SQL like Prometheus or Elasticsearch), files and HTTP APIs. It is not a SQL layer on top of these various APIs like FDW in Postgres or Apache Calcite. For Prometheus you query with PromQL. For Elasticsearch you query with Lucene. And for SQL databases you query with their SQL dialect.

DataStation is made of panels (other apps might call them cells) that each produce a result. Panels can refer to other panels. These allow you to build workflows that cross the boundary of a particular datasource. For example you might have some data in a CSV a product manager gave you and the bulk of your data is in PostgreSQL. DataStation helps you pull together these data sets and script them.

DataStation is mainly a desktop app today where the end result is that you export graph SVGS or HTML tables or markdown tables or just a CSV file. All this data stays on your laptop so it's as easy to use in a corporate environment as any existing SQL IDE or Jupyter Notebook.

In the last year it's reached 1.5k stars on Github, over 1000 unique users and currently on-average about 40 fairly active users per month (defined as having opened the app more than a few times).

DataStation is primarily an Electron app but the code that evaluates panels is written in Go.

You can find a ton of tutorials on how to interact with supported databases on the DataStation website: https://datastation.multiprocess.io/docs/.

Looking forward to your feedback!

There are primarily a bunch of tutorials for getting started [0] (these are up to date) and some old videos (not up to date) [1].

[0] https://datastation.multiprocess.io/docs/

[1] https://www.youtube.com/channel/UCGOQFKonPUVo5LgxQDW26yg/vid...

A markdown generator embeds markdown from a Github repo into the marketing site. This way the marketing site is kept private while anyone can easily contribute to docs.

Docs are kept in separate folders for each release.

https://github.com/multiprocessio/datastation-documentation

Support for Kerberos keytab files

JuiceFS under the Apache License 2.0, is a high-performance POSIX file system optimized for cloud-native environments. It stores data in Object Storage (e.g., Amazon S3) and metadata in databases like Redis, MySQL, or TiKV. JuiceFS integrates massive cloud storage with big data, machine learning, and AI applications efficiently, akin to local storage. It features full POSIX and Hadoop compatibility, S3 interface, Kubernetes support, and shared file storage for numerous clients. Some cool features are - strong consistency, scalable performance, data encryption, global file locks, and compression with LZ4 or Zstandard.

To improve the handling process of capacity overrun, the JuiceFS client supports deletion operations in the case of Ceph cluster fullness (see related code changes in JuiceFS Community Edition). Therefore, for newer client versions, there is no need to use set-full-ratio for temporary adjustments.

If you have any questions or would like to learn more, feel free to join discussions about JuiceFS on GitHub and the JuiceFS community on Slack.

The architecture image shows GCS and others, so I suspect it does.

https://github.com/juicedata/juicefs#architecture

See also: JuiceFS: https://juicefs.com/

Adds a DBMS or key-value store for metadata, making the filesystem much faster (POSIX, small overwrites don't have to replace a full object in the GCS/S3 backend).

Almost certainly a better solution if you want to turn your object storage into a mountable filesystem, with the (big) caveat that you can't access the files directly in the bucket (they are not stored transparently).

JuiceFS can do the same thing as s3fs-fuse, but better. Because it supports robust data consistency and caching policies to improve performance.