Status, September 2026: Amundsen is archived. The Amundsen repository carries the notice “Due to inactivity, this project was archived in September 2026. The contents will remain available for historical purposes,” and GitHub records the archive on September 10, 2026. The code stays public under Apache-2.0 and every step below still runs against it, so this guide is for someone maintaining an existing Amundsen deployment: there are no new releases, no security patches, and no maintainer review of issues or pull requests. If you are standing up a catalog for the first time, start on a maintained project instead, such as DataHub or OpenMetadata.
Amundsen Setup refers to installing and configuring Amundsen, an open-source platform for data discovery and metadata management.
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It centralizes metadata so data assets are easier to find and search.
The setup deploys Amundsen with tools like Docker Compose and configures metadata ingestion pipelines.
Connecting it to data sources and to platforms like Apache Airflow puts shared metadata in front of the people who need it.
Because the project is archived, whatever you run you now maintain yourself, patches included.
Quick answer:
Crunched for time? We’ve got you covered with a quick summary of the highlights of this article:
- The article takes a comprehensive look at Amundsen’s architecture and setup process.
- The hosted demo environment below shows Amundsen as it stood when the project was archived.
- With Amundsen archived, a catalog someone else maintains is worth pricing out. Atlan has been recognized as a leader in enterprise data catalogs. Book a demo or watch the Context Engineering Studio demo.
In this guide, we’ll walk through all the requisite steps to set up Amundsen, which was one of the most widely deployed open-source data catalogs before it was archived.
For ease of understanding, the setup process has been sequentially divided into the following steps:
- Taking stock of requirements to install Amundsen
- Understanding Amundsen architecture
- Cloning the Amundsen repository
- Loading sample data
- Using standalone python scripts
- Enabling Elasticsearch and Atlas
- Anticipating common installation issues
Amundsen Data Catalog Demo
Here’s a hosted demo environment showing the Lyft Amundsen data catalog as it stood when the project was archived:
Requirements to install Amundsen
To install Amundsen, you would need the following:
- A local instance or a cloud VM (for instance, on AWS, Google Cloud, or Azure) running Docker and Docker Compose.
- Python ≥ 3.6, Node v10 or v12, npm ≥ 6.x.x, and Elasticsearch 6.x - all of these will be installed using Docker Compose.
Amundsen architecture
Amundsen is a composite of five different components serving different purposes by interacting with each other:
- Frontend - uses React and Flask to provide an interface for you to use other components of Amundsen. The front end is accessible at localhost:5000.
- Search - uses Elasticsearch to index metadata and enables the users accessing the Frontend to search the metadata. The search service is accessible at localhost:9200, but there’s no UI for it.
- Metadata - can choose to store and retrieve data from a host of database backends, including neo4j, MySQL on RDS, Apache Atlas, etc. The metadata service is accessible at localhost:21000.
- Databuilder - helps you extract metadata from different data sources and load them into the metadata backend. You can use this library with standalone Python scripts or an orchestrator like Airflow.
- Common - holds the code that is used across all the microservices mentioned above.
You can read more about the original implementation at Lyft on their engineering blog.
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How to set up Amundsen?
Follow the following steps:
- Step 1- Clone the Amundsen repository from GitHub
- Step 2- Load sample data using sample data loader scripts
- Step 3- Clean existing docker
- Step 4- Increase docker-engine memory to more than the default
Let’s look at each step in detail.
Step 1: Cloning the Amundsen repository from GitHub
Although you can install the microservices mentioned above in a standalone fashion and configure them to talk to each other, the easiest way to set up Amundsen is by using Docker Compose to do all that work for you. Let’s first clone the Amundsen repository on your local instance or the newly created VM.
git clone --recursive https://github.com/amundsen-io/amundsen.git
You’ll find several docker-amundsen-*.yml files in the root directory of the repository. The only major difference in the different YAML files is the different backend used for the metadata service.
As mentioned earlier, you can choose a host of databases to store and retrieve your metadata. The original and default option is neo4j, but it only allows for pull-based metadata collection.
On the other hand, Apache Atlas allows for both push-based (via Atlas Hive Hook) and pull-based metadata collection. Atlas has many other benefits as it is a full-fledged data governance service that allows you to view and edit your metadata using the Atlas UI.
Read more about Apache Atlas here.
For the reasons mentioned above, we’ll use Apache Atlas as the backend. Let’s install Amundsen using the following command:
docker-compose -f docker-amundsen-atlas.yml up
Following the command, you will see a screen similar to the one shown below, where all the microservices are deployed:
Install Amundsen using Docker Compose - Source: Amundsen.
The installation process will take a while to complete. You can check the status of the individual services by running the docker ps command as shown below:
Check the status of Docker containers for all the microservices - Source: Amundsen.
Although the services will be available pretty quickly, as shown by the docker ps command, you won’t see anything if you visit Amundsen UI because Atlas takes a while to get started for the first time. You’ll see the following screen when Atlas is being started:
The last leg of the installation — Apache Atlas takes some time to start - Source: Amundsen.
Step 2: Loading sample data
Before logging into the Amundsen UI, you must load some sample data into the backend.
Pro tip: Although the documentation says you don’t need to load sample data if you’re installing Amundsen with Atlas backend, it doesn’t quite work. You might end up with an empty Amundsen screen with no data loaded. To rectify this installation glitch, you can use one of the pre-built scripts to load sample data in any given backend.
Using standalone python scripts
You can use the databuilder library with standalone Python scripts and orchestration tools like Airflow to keep the metadata updated based on a schedule. Think of it as a library to support an ETL job that just extracts, transforms, and loads metadata from a data source. For simplicity’s sake, let’s just use one of the sample data loader scripts available to load the sample data into the metadata database. For loading data into Atlas, use the sample_data_loader_atlas.py file as follows:
cd amundsen/databuilder
python3 -m venv venv
source venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
python3 setup.py install
python3 example/scripts/sample_data_loader_atlas.py
Elasticsearch and Atlas
The script mentioned above will not only load data into Atlas, but it will also create the three default indexes on the Elasticsearch instance —table_search_index, user_search_index, and dashboard_search_index. This script will take a few minutes to execute.
Populate Elasticsearch indexes - Source: Amundsen.
Once this script completes successfully, you can be rest assured that data has been pushed to both Elasticsearch and Atlas and is ready to be queried from both the Atlas and Amundsen frontends. You can access the data via the Amundsen UI on localhost:5000 as shown below:
Amundsen Table Navigation - Source: Amundsen.
Alternatively, you can also access your metadata using the Atlas UI at localhost:21000, as shown below:
Apache Atlas with the legacy frontend- Source: Amundsen.
Common installation issues
Aside from the installation issues mentioned in the official documentation, a few issues might come up depending on where you are trying to install Amundsen. We’ll look at some of those issues and their resolutions.
Step 3: Clean existing docker
To avoid random preventable errors such as, ERROR: readlink /var/lib/docker/overlay2: invalid argument make sure that you remove all the unused containers using docker system prune. This will remove all unused containers, networks, and images and reclaim the freed-up space from those entities. By default, the volumes aren’t removed to prevent you from deleting important data accidentally. If you don’t have any important data, you can run docker volume prune to clean up all the volumes. Your Docker will be fresh as a daisy now.
Step 4: Increase Docker engine memory
If you’re installing this on your local machine (for instance, a Mac), you might want to reset your Docker Engine Memory from the default 2GB to a bit more because Docker Compose might run out of memory.
Adjust memory allocation settings in Docker Preferences to prevent memory issues with Docker Compose - Source: Amundsen.
Amundsen demo: Explore and get a feel for Amundsen with a pre-configured sandbox environment
Now that you have set up Amundsen, you can explore the features with the loaded sample data. To see real data, connect one or more of your data sources. Amundsen also previews the data stored in the tables while you search the metadata database.
If you would rather skip the install and look around first, the sample demo environment below shows the archived release:
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FAQs about Amundsen setup
1. What is Amundsen Setup?
Amundsen Setup refers to the process of installing and configuring Amundsen, an open-source data discovery and metadata platform built at Lyft. The project was archived in September 2026, so the code is still public under Apache-2.0 but the repository is read-only. Installing it today makes sense if you are keeping an existing deployment running, not if you are choosing a catalog.
2. How do I configure Amundsen for data discovery?
To configure Amundsen for data discovery, you’ll need to follow a structured setup process. This includes preparing your environment, using Docker Compose for installation, and connecting Amundsen to your data sources and pipelines for metadata indexing. Everything runs against the archived September 2026 code, so pin your versions and expect to fix breakages yourself.
3. What are the key steps in setting up Amundsen?
Setting up Amundsen involves several steps: Installing prerequisites like Docker, configuring Docker Compose with Amundsen services, setting up metadata ingestion pipelines, and verifying that all components—frontend, backend, and search—are working properly.
4. How does Amundsen integrate with existing data pipelines?
Amundsen’s databuilder library ships extractors for pipelines such as Apache Airflow and Kafka, and it can ingest metadata from them on a schedule. Those extractors are frozen at the September 2026 archive, so newer versions of the systems they talk to may need patches you write and maintain.
5. Can Amundsen Setup be automated?
Yes, Amundsen Setup can be automated using scripting and orchestration tools. You can use configuration files and automated deployment pipelines to streamline the installation process, ensuring consistency and reducing manual errors.
6. What are the best practices for optimizing Amundsen Setup?
To optimize Amundsen Setup: Ensure that metadata ingestion pipelines are properly configured, update the configuration as your data sources change, and monitor system performance so you can size the infrastructure correctly. Security needs the most attention. No upstream patches have shipped since the project was archived in September 2026, so you own CVE tracking and fixes for Amundsen and for its Elasticsearch, Neo4j, and Atlas dependencies. If that ownership is more than your team wants, move to a maintained project such as DataHub or OpenMetadata.
