End-to-End ELT Pipeline

Overview

This repository contains a data pipeline designed to automate the process of extracting data from a source database, loading it into a destination database, and then performing data transformations using dbt (data build tool).

Key Features

• Data Extraction: Extracts data from a specified source database.
• Data Loading: Loads the extracted data into the destination database for further processing.
• Data Transformation with dbt: Utilizes dbt to transform the loaded data, ensuring it is optimized and ready for analysis.

Workflow

1. Extract: The pipeline connects to the source database, extracts the necessary data, and prepares it for loading.
2. Load: The extracted data is then loaded into the destination database, setting the stage for transformation.
3. Transform: dbt is employed to transform the loaded data, applying necessary business logic and preparing the data for analytics or reporting.

Technologies Used

• Source Database: [Specify Database Type, e.g., MySQL, PostgreSQL]
• Destination Database: [Specify Database Type, e.g., PostgreSQL, Redshift]
• dbt: For data transformation and modeling
• Docker: To containerize the environment for consistent and isolated execution.
• Subprocess: For running command-line processes programmatically within the pipeline.
• Cron Job: For scheduling and automating the pipeline execution at specified intervals.

How to Run

Prerequisites

• Ensure that Docker is installed and running on your system.
• Install dbt and configure it for the destination database.

Steps

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Configure the connection settings for both the source and destination databases. Update the configuration files in the config/ directory.

3. Configure dbt:

   pip install dbt-core dbt-postgres --user
   

4. Build and run the Docker containers:

   docker-compose up --build

5. Run the pipeline:

   • The pipeline can be executed manually using the command:

     python run_pipeline.py

   • Alternatively, you can set up a cron job to run the pipeline automatically at scheduled intervals. Example:

     0 0 * * * /usr/bin/python3 /path/to/your/repo/run_pipeline.py

6. Stop the Docker containers and remove volumes:

   docker-compose down -v

. Monitor and review the transformation results: Check the destination database for the transformed data, or review dbt logs and reports for more details.

Documentation

Pipeline Configuration: Detailed information about configuring the source and destination databases can be found in the docs/configuration.md file.

• Running dbt: Refer to the docs/dbt.md file for instructions on setting up and running dbt within the pipeline.
• Docker Setup: Instructions for setting up and troubleshooting Docker can be found in the docs/docker.md file.

Tech Stack

• Docker: To containerize the application and ensure consistent environments.
• Python: For writing and managing the pipeline scripts.
• Subprocess: To manage the execution of command-line tools within the pipeline.
• SQL: For querying and managing data within databases.
• Bash: For scripting and automation tasks.
• Cron Job: To schedule automated execution of the pipeline.
• dbt: For data transformation and modeling.

Contact

For questions or support, please contact ME at devvardhan456@gmail.com