AWS Cloud Data Pipeline

Key Insights

• Serverless architecture reduced operational complexity while maintaining scalability across the analytics workflow.
• Converting raw CSV files to Parquet improved query performance and reduced storage and analytics costs.
• Event-driven automation using S3 triggers and Lambda eliminated manual pipeline execution and improved reliability.

Project Overview

This project involved designing and deploying an end-to-end analytics pipeline using Amazon Web Services (AWS). The objective was to move beyond traditional data analysis workflows and implement a cloud-native architecture capable of automated ingestion, transformation, storage, cataloging, and SQL-based analytics.

Using AWS services including S3, Lambda, Glue, Athena, and the Glue Data Catalog, the team built a serverless workflow that automatically processes raw datasets, transforms them into optimized Parquet files, and makes them available for analysis through SQL queries.

The project emphasized architectural design, IAM-based security, cloud best practices, automation, and cost-efficient analytics using modern data engineering concepts.

What I Worked On

• Helped define the overall pipeline architecture and analytics workflow, evaluating AWS services and shaping the serverless design approach adopted by the team.
• Coordinated project planning and team collaboration in a Scrum-style workflow.
• Participated in designing the data lake structure using separate RAW and CLEAN zones within Amazon S3.
• Contributed to ETL workflow design using AWS Glue and Parquet-based optimization strategies.
• Configured and tested integrations across S3, Lambda, Glue, Athena, Crawlers, and the Glue Data Catalog.
• Evaluated architectural alternatives including Amazon RDS and EC2 before selecting a fully serverless solution.
• Presented the project objectives, architecture overview, and final conclusions during the team presentation.

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