Why Choose This Project?

Building a serverless data lake allows you to store and analyze massive amounts of structured and unstructured data at scale without managing servers or infrastructure. Using Amazon S3 for storage and AWS Glue for ETL (Extract, Transform, Load), you can create a powerful, scalable, and cost-effective solution for big data analytics.

This project is ideal for data engineering, analytics, or cloud computing use cases where you need to unify data from multiple sources, clean it, and make it queryable using tools like Athena, Redshift, or SageMaker.

What You Get

• Fully serverless architecture with no infrastructure management

• Scalable storage of raw and processed data in S3

• Automated data cataloging using AWS Glue Crawlers

• ETL pipelines using AWS Glue (PySpark)

• Query-ready datasets using Athena or Redshift Spectrum

• Pay-per-use model for cost efficiency

Key Features

Technology Stack

Cloud Services Used

Working Flow

1. Raw Data Ingestion

   • Upload raw data (CSV, JSON, Parquet, logs, etc.) to Amazon S3 "landing zone"

2. Glue Crawler Execution

   • Automatically crawl S3 and update metadata in AWS Glue Data Catalog

3. Glue ETL Job

   • Run PySpark scripts to clean, transform, enrich, or join data

   • Output to a “curated” S3 bucket (structured and optimized)

4. Cataloging Processed Data

   • Crawler catalogs output data and maintains versioned schemas

5. Query with Athena / Redshift

   • Use SQL to analyze the data directly from S3

   • Integrate with BI tools like QuickSight, Tableau, Power BI

6. Orchestration

   • Trigger ETL jobs using EventBridge (e.g., daily schedule or S3 upload)

Main Modules

Security Features

• IAM Roles & Least Privilege access for Glue, S3, Athena

• S3 Bucket Policies for fine-grained access control

• Server-side encryption using AWS KMS

• Logging with CloudTrail for data access

• Tagging & Resource Groups for auditing and cost tracking

Visualization Options