Experience lakeFS first hand with your own isolated environment. You can easily integrate it with your existing tools, and feel lakeFS in action in an environment similar to your own.
Get a local lakeFS instance running in a Docker container. This environment includes lakeFS and other common data tools like Spark, dbt, Trino, Hive, and Jupyter.
As a prerequisite, Docker is required to be installed on your machine. For download instructions, click here
The following commands can be run in your terminal to get the Bagel running:
- Clone the lakeFS repo:
git clone https://github.com/treeverse/lakeFS.git
- Start the Docker containers:
cd lakeFS/deployments/compose && docker compose up -d
Once you have your Docker environment running, it is helpful to pull up the UI for lakeFS. To do this navigate to
http://localhost:8000 in your browser. The access key and secret to login are found in the
docker_compose.yml file in the
Once you are logged in, you should see a page that looks like below.
The first thing to notice is in this environment, lakeFS comes with a repository called
example already created, and the repo’s default branch is
main. If your lakeFS installation doesn’t have the
example repo created, you can use the green
Create Repository button to do so:
Learn how to use lakeFS using the CLI and an interactive Spark shell - all from your browser, without installing anything.
In the tutorial we cover:
lakectlcommand line usage
- How to read, write, list and delete objects from lakeFS using the
- Read from, and write to lakeFS using its S3 API interface using Spark
- Diff, commit and merge the changes created by Spark
- Track commit history to understand changes to your data over time
The web based environment provides a full working lakeFS and Spark environment, so feel free to explore it on your own.