Modern Data Integration Tool

A multiheaded modern data bridging package based on pipeline manifests to integrate between any modern (and old) data stack tools

Setup

Quick Install

python -m pip install mdit

Build from source

Clone the repository

git clone https://github.com/Broomva/mdit.git

Install the package

cd mdit && make install

Build manually

After cloning, create a virtual environment

conda create -n mdit python=3.10
conda activate mdit

Install the requirements

pip install -r requirements.txt

Run the python installation

python setup.py install

Usage

The deployment requires a .env file created under local folder:

touch .env

It should have a schema like this:

databricks_experiment_name=''
databricks_experiment_id=''
databricks_host=''
databricks_token=''
databricks_username=''
databricks_password=''
databricks_cluster_id=''

import mdit 

# Create a Spark session
spark = DatabricksSparkSession().get_session()

# Connect to MLFLow Artifact Server
mlflow_session = DatabricksMLFlowSession().get_session()