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homebase_assignment

Heart Disease Data Analysis Repository

This repository contains scripts and configurations for extracting, transforming, loading (ETL), and analyzing heart disease data.

Prerequisites

Before proceeding, ensure you have the following prerequisites installed:

  • Python 3.8 or higher
  • Airflow
  • PostgreSQL database
  • ClickHouse database

Data Extraction and Transformation

  1. Extract data from source:

    Run the etl.py script to extract data from the source system and load it into a staging table in PostgreSQL.

    python etl.py

Data Loading and Analysis

  1. Create ClickHouse DAG:

    Copy the postgres_to_clickhouse_dag.py file to the DAGS folder of your Airflow installation. This file defines an Airflow DAG that will periodically transfer data from PostgreSQL to ClickHouse.

    bash cp postgres_to_clickhouse_dag.py /

  2. Trigger ClickHouse DAG:

    Trigger the ClickHouse DAG to load data from PostgreSQL to ClickHouse. You can do this manually from the Airflow web interface or schedule it to run automatically.

  3. Analyze data in ClickHouse:

    Use the heart_disease_query.sql file to explore and analyze the heart disease data in ClickHouse. This file contains sample queries to get insights into the data.

Contributing

Contributions are welcome! Please feel free to open issues or pull requests with suggestions, improvements, or bug fixes.

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