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Data Pipeline with Databricks

PURPOSE

The purpose of this repo is to develop a data pipeline using Databricks. The primary goal is to create an efficient and functional pipeline that includes at least one data source and one data sink.

Dataset: Iris Dataset


PROCESS

The code performs ETL-Query operations:
Extract (E): Extract a dataset from a URL in CSV format.
Transform (T): Utilizes Spark SQL to amalgamate two datasets, ensuring they are primed for analysis.
Load (L): Transfers the transformed data into the destination data store, leveraging Delta Lake.


Commands to Run the Repo

To run the project, you can use the Makefile and follow these commands:

  1. # To install the required the python packages
    make install
    
  2. # To check code style
    make lint
    
  3. # To run tests
    make test
    
  4. # To format the code
    make format
    

Workflows

On running the above commands, it runs successfully.

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