Azure-ELT

Azure Data Pipeline for ETL with Databricks

Table of Contents

1. Project Overview
2. Architecture
3. Data Flow
4. Setup Instructions
5. Usage
6. Technologies Used
7. Contributing
8. License

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Project Overview

This project establishes an ETL (Extract, Transform, Load) pipeline using Azure and Databricks. The pipeline fetches raw data from various sources into Azure Blob Storage, where it’s then processed, cleaned, and transformed in Databricks using Python and SQL. This pipeline architecture supports scalable and efficient data operations, suitable for complex data transformation and analysis tasks.

Key Features

• Data Ingestion: Fetches data into Azure Blob Storage.
• Data Processing and Transformation: Utilizes Databricks for data cleaning and transformation.
• Language Support: Python and SQL for data manipulation.
• Scalability: Built to scale with large datasets.

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Architecture

The project architecture involves the following Azure components:

1. Azure Blob Storage: Centralized storage for raw data.
2. Databricks: Platform for data transformation and analysis using Python and SQL.
3. Azure Data Factory (optional): For scheduling and orchestrating data workflows.

Pipeline Flow:

1. Data is fetched and ingested into Azure Blob Storage.
2. Databricks processes data for cleaning and transformation.
3. Transformed data is stored for further analytics or applications.

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Data Flow

1. Data Collection: Data is sourced from various APIs or databases and moved to Azure Blob Storage.
2. Data Transformation: In Databricks, data is cleaned and transformed using Python and SQL.
3. Storage and Output: The transformed data is stored in Azure Blob Storage or other data sinks for downstream applications or analysis.

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Setup Instructions

Prerequisites

• Azure Account
• Databricks Workspace
• Python 3.x

Steps

1. Set up Azure Blob Storage:
   • Create a storage account and a container to store the raw data.
2. Configure Databricks:
   • Set up a Databricks workspace and a cluster.
   • Install necessary libraries (pandas, sqlalchemy, etc.).
3. Connect Azure Blob Storage to Databricks:
   • Generate a Shared Access Signature (SAS) for secure access.
4. Write Data Ingestion Script:
   • Script to fetch data into Azure Blob Storage.
5. Data Transformation in Databricks:
   • Use notebooks to clean and transform data using Python and SQL.

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Usage

1. Ingest Data:
   • Run the ingestion script to pull data into Azure Blob Storage.
2. Transform Data in Databricks:
   • Open Databricks, load the notebook, and run cells to transform the data.
3. Export Transformed Data:
   • Store the processed data for further use.

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Technologies Used

• Azure Blob Storage: Data storage
• Azure Databricks: Data transformation
• Python: Data cleaning and manipulation
• SQL: Data querying and transformation

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Contributing

Contributions are welcome! Please open an issue or submit a pull request.

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License

This project is licensed under the MIT License.