This project demonstrates a complete serverless data analytics pipeline on AWS, processing and visualizing data using managed services. The pipeline ingests raw data, transforms it, makes it queryable, and creates interactive visualizations—all without managing any servers.
The solution showcases modern data engineering practices using AWS's serverless analytics services, providing a scalable and cost-effective approach to data analytics.
What it is
A serverless data analytics pipeline featuring:
- Data Storage: Amazon S3 as the central data lake for raw and processed data.
- Data Cataloging: AWS Glue for discovering, cataloging, and transforming data.
- Data Querying: Amazon Athena for interactive SQL queries on S3 data.
- Data Visualization: Amazon QuickSight for creating interactive dashboards and reports.
- ETL Processing: Automated data transformation using AWS Glue ETL jobs.
Key Technical Details
- Serverless Architecture: Fully serverless pipeline with no infrastructure to manage.
- Data Lake: S3-based data lake following best practices for data organization.
- Schema Discovery: Automatic schema detection and cataloging using Glue crawlers.
- Query Performance: Optimized data formats (Parquet) and partitioning for efficient queries.
- Visualization: Interactive dashboards with drill-down capabilities in QuickSight.
- Cost Optimization: Pay-per-query pricing model, only paying for resources used.
- Scalability: Automatically scales to handle datasets of any size.
What I Learned
- Data Lake Architecture: Designing and implementing a data lake on S3 with proper organization.
- Serverless Analytics: Building analytics pipelines without managing servers or clusters.
- AWS Glue: Using Glue for ETL, data cataloging, and schema management.
- Query Optimization: Techniques for optimizing query performance in Athena.
- Data Visualization: Creating effective dashboards that provide actionable insights.
- Cost Management: Understanding and optimizing costs in serverless analytics workloads.