Snowflake_Project

End-to-End Data Engineering Project with Snowflake & Azure

🛠️ Project Overview

This project focuses on implementing a modern data engineering solution for an e-commerce company with over 5M customers, addressing challenges in data management, processing delays, scalability, data quality, and advanced analytics.

The solution leverages Snowflake and Azure Data Lake Storage (ADLS) to build a robust data pipeline that centralizes data, enables near real-time analytics, and supports advanced business insights.

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🚀 Business Problem

The e-commerce company faced the following challenges:

1. Data Silos: Fragmented data across systems.
2. Processing Delays: 24-hour delays in batch processing.
3. Scalability Issues: On-prem infrastructure unable to handle increasing data volumes.
4. Data Quality Problems: Inconsistent data formats and lack of standardization.
5. Limited Analytics: Insufficient support for advanced analytics and machine learning initiatives.

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🎯 Project Goals

• Centralized Data Source: Break down data silos.
• Real-Time Analytics: Reduce processing delays.
• Scalability: Handle growing data volumes with ease.
• Enhanced Data Quality: Standardize and clean data.
• Advanced Analytics: Support machine learning and personalized marketing.

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🔧 Architecture Overview

Data Flow:

1. Source Systems:
   • Customer Data: Daily CSV files from CRM.
   • Product Data: Hourly JSON updates from inventory systems.
   • Transactional Logs: Real-time Parquet files from the website.
2. Pipeline:
   • Data → Azure Data Lake Storage (ADLS) → Snowflake → BI Tools

Multi-Layer Architecture:

• Bronze Layer: Raw data ingestion.
• Silver Layer: Data cleaning and transformations.
• Gold Layer: Business-level aggregates for reporting and analytics.

Key Snowflake Features:

• External Storage: Connecting ADLS to Snowflake for seamless data integration.
• COPY Command: Efficient data loading.
• Streams & Tasks: Automating incremental data loading and transformations.

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🧹 Data Transformations

Examples of cleaning and standardization tasks:

• Email Validation: Ensure email is not null.
• Customer Type Standardization: Normalize to Regular, Premium, or Unknown.
• Age Validation: Validate age is between 18 and 120.
• Gender Classification: Standardize gender to Male, Female, or Other.

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✅ Project Outcomes

• Reduced data processing delays from 24 hours to 1 hour.
• Improved data quality and reporting accuracy.
• Scalable infrastructure to handle peak loads.
• Enabled real-time analytics and set the foundation for advanced ML use cases.

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🛠️ Tools & Technologies

• Cloud Platform: Snowflake, Azure Data Lake Storage (ADLS)
• Data Formats: CSV, JSON, Parquet
• Data Engineering Features: Streams, Tasks, COPY Command
• Reporting Tools: BI Platforms (e.g., Power BI, Tableau)

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📂 Repository Structure

├── data_sources/          # Sample input data (CSV, JSON, Parquet)  
├── sql_scripts/           # SQL scripts for transformations and data loads  
├── notebooks/             # Jupyter notebooks for exploration and validation  
├── docs/                  # Documentation and design diagrams  
├── config/                # Configuration files for Snowflake and ADLS  
└── README.md              # Project overview