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Data Overload Vol. 7

Data Overload Vol. 7

Author
Juan Luis Cano
Date
March 1, 2022

Welcome to Data Overload Vol. 7, the monthly newsletter with company updates from Orchest!

Road to Austin ✈️

The company is hands on deck to prepare for the upcoming Data Council in Austin, Texas (USA), happening on March 23rd and 24th. Rick, our CEO, will present there a Quick Tour of the Orchest Open Source project, which will include several surprises we have been working on in the past months.

Opening our newsletter 📣

Wondering where the previous editions are? We used to send them to our email subscribers, but from now on we will publish it on our blog as well. If you want to receive it directly on your inbox, check out the subcription form in our landing page.

Thank you!

Thank you to all our users and folks sending feedback on our public Slack workspace and our GitHub repository. You help us make Orchest better every day.

- Juan Luis Cano (Data Scientist Advocate)

Product updates

✨ New environment edit page

More intuitive container image and runtime selection.

New environment edit page

⤵️ New "import project" dialog

Easier than ever! Look for an "Open in Orchest" button near you, like the ones we added to our examples.

New "import project" dialog

🔔 New version upgrade prompt

So that you don't miss a single update!

New version upgrade prompt

What we're reading

  • Tiny wins (blog post)

    Why we like it: This classic post showcases real reactions from users to small, but impactful UI changes done on GitHub, Netflix, and Google Chrome over the years. Joel reflects on those and outlines strategies to prioritize low-effort, high-impact changes, and gives some ideas on how to source those tiny wins. A fantastic read!
  • How we built Instant Logs (blog post)

    Why we like it: Ben from Cloudflare describes the limitations of their logging architecture, and explains how they leveraged a smart technique called Reservoir Sampling to shard the logs efficiently. An insightful walkthrough on the kind of problems that appear at a sheer scale.

About Orchest

Orchest is an open-source project that simplifies the development and deployment of data pipelines - check out Orchest on GitHub.