Skip to content

Installation

Core Installation¤

We recommend uv to manage your Python environment and packages, but conda, venv or other manager can also be used.

First, ensure you have installed UV, and make sure you have created and activated a Python 3.9+ virtual environment.

Then, run:

uv pip install laktory

You now have Laktory python package and CLI installed.

First, ensure you have created and activated a Python 3.9+ virtual environment.

Then, run:

pip install laktory

You now have Laktory python package and CLI installed.

Installation Validation¤

To verify the installation, open a terminal with the virtual environment activated and execute:

>>> laktory --version
Laktory CLI version 0.5.13

Optional Dependencies¤

In the spirit of having a package that is as lightweight as possible, only a few core dependencies will be installed by default:

  • networkx: Creation manipulation of networks for creating pipeline DAG.
  • pydantic: All laktory models derived from Pydantic BaseModel.
  • typer: Library for building CLI applications.

This core only allows to declare data pipelines and stacks. For an execution or a deployment, one of the optional dependencies must be installed.

Dataframes¤

If you want to run your pipeline locally or test some of the transformations, you will have to install the dataframe library used by your transformations. Available options are spark and polars.

  • Apache Spark 

    uv pip install laktory[spark]
    For running spark locally, you also need to follow instructions provided here. If you use homebrew to install java, your JAVA_HOME and SPARK_HOME environment variables should look something like:
    • JAVA_HOME=/opt/homebrew/opt/java
    • SPARK_HOME=/opt/homebrew/Cellar/apache-spark/3.5.0/libexec

  • Polars 

    uv pip install laktory[polars]

Infrastructure as Code¤

If you use Pulumi as the IaC backend, you will want to run 

uv pip install laktory[pulumi]

If you use Terraform, it has to be installed manually because it's not python-based and can't be installed from pip.

Orchestrator¤

If you want to run your pipeline remotely using one of the supported orchestrator you will have to install their respective packages.

  • Databricks 
    uv pip install laktory[databricks] databricks-connect

Cloud Provider¤

The DataEvent class lets you write data events to various cloud storage accounts, but requires to install additional dependencies.

  • Microsoft Azure: 

    uv pip install laktory[azure]

  • Amazon Web Services (AWS) 

    uv pip install laktory[aws]

Git-based installation¤

If you need or prefer installing Laktory from git, you can use: 

pip install git+https://github.com/okube-ai/laktory.git@main