Library / Case Studies

Data Orchestration Service at Spotify

TL;DR Within Spotify, we run 20,000 batch data pipelines defined in 1,000+ repositories, owned by 300+ teams — daily. The majority of our pipelines rely on two tools: (for the Python folks) and (for the Java folks). In 2019, the data orchestration team at Spotify decided to move away from these tools. In this post,…

By Spotify engineers · Spotify · Added 2026-10-12

Open original source

All content belongs to Spotify engineers / Spotify. We only link to it and give credit. Report a problem

Learn this case study

About 37 min left. Your work is saved as you go.

What you are about to study

Spotify explains a real system. You should know the basics: caching, queues and replication.

Ideas you will meet

Machine Learning Systems
Recommendations, ranking, feature stores and serving models at scale.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

Want the architecture first? Read our Spotify case study, with its diagram.

Go deeper on this

A path through videos, articles, other case studies and code on the same ideas as Data Orchestration Service at Spotify.

  1. ReadEngineers explain it in their own words.

  2. WatchHow others solve the same problem, on video.

    Videos belong to their creators and play from YouTube. More on YouTube (opens in a new tab)

  3. See it in another companyThe same ideas, a different system.

Keep learning

Loading community…