What is Amundsen?
Amundsen is a data discovery platform and metadata engine that was developed at Lyft to address the common pain points faced by their data scientists, engineers, and researchers in their typical workflows.
Homegrown by the Lyft engineering team, Amundsen was named after Norwegian explorer Roald Amundsen.
Amundsen has improved the productivity of data scientists, analysts, and researchers at Lyft by ~20%
Why did Lyft build Amundsen?
Lyft reported an active rider base of 13.49 million in the first quarter of 2021. Now, imagine this number, in turn, generating a tremendous amount of data to be stored, processed, and analyzed, and also the huge number of people who might be using this data daily to make informed decisions.
At a fundamentally modern data-driven company like Lyft, every interaction is powered by data, and it's impossible to scale sustainably if the data teams are not empowered to productively and effectively use this data.
Lyft recognized this challenge and developed Amundsen, which they introduced in April 2019 as a solution to their data discovery woes.
Is Amundsen open source?
Amundsen was open sourced in October 2019, a year following its launching in production at Lyft & is licensed under the Apache License, Version 2.0. A copy of the license can be found here. Here's a roundup of the permissions, limitations and conditions that govern the license.
How does Amundsen work?
- Easy discovery of trusted data
- Automated & curated metadata
- Ability to share context with coworkers
- Learn and understand from data usage
Easy discovery of trusted data
Amundsen helps find data within an organization by a simple text search. The page-rank inspired algorithm returns with popularity ranking and also recommendations.
Automated and curated metadata
When a data asset is clicked on, users are shown its detailed description and its behaviour. Information like descriptions and tags are manually entered by users, while information like popular users is generated automatically by grazing through the audit logs.
Ability to share context with coworkers
One can update descriptions to data assets, thus reducing back and forth between co-workers looking for more context behind a particular data.
Learn and understand from data usage
Users can see which data assets get frequently used, owned, or bookmarked. One can even understand the most common queries for a table by seeing dashboards built on a given table.
The Amundsen Architecture
- Amundsen consists of five major components and follows a
- Metadata Service: Able to handle requests from both frontend service and microservices
- Search Service: Backed by elastic search
- Frontend Service: Hosts the web application
- Databuilder: Ingestion framework which extracts metadata from various sources
- Common: Library repo which holds common codes among microservices
Democratizing Data Discovery at Lyft
Amundsen is used by 750 data users at Lyft
True democratization is possible when everyone looking for data resources know exactly what data is available within the system & how they can use it, but that may also pose challenges with respect to data privacy & security. Amundsen seeks to walk the balance between democratization and security by classifying metadata into two groups:
Fundamental metadata like name and description of table and fields, owners, last updated, etc. are visible to all. This enables users to find of its existence and also to understand if it fits their query.
Richer metadata like column stats, preview, etc. are only available to users with access to data. One can also request access to richer metadata if they are convinced that it's the right fit for them.