AI Data Pipeline Automation

A serverless, PII-compliant data pipeline for AI experimentation and analytics.

40%

ETL Runtime Reduction

1 TB+

Data Processed

20%

Cost Reduction

Technologies

AWS GlueAWS LambdaAmazon AthenaAmazon MacieAmazon SageMakerS3Python

Problem

Data pipelines required manual intervention for schema discovery, query validation, and compliance scanning. ETL processes were slow and lacked automated PII detection for large PostgreSQL exports.

Solution

Built a serverless data pipeline using AWS Glue for ETL, Lambda for orchestration, Athena for querying, and Macie for automated PII scanning. Implemented Glue Crawlers for schema discovery and S3 lifecycle policies for cost optimization.

Architecture

AWS Glue for ETL processing and schema discovery

Lambda functions for orchestration and automation

Athena for SQL querying and data analysis

Macie for automated PII detection and compliance scanning

S3 with intelligent lifecycle policies for cost optimization

SageMaker integration for AI experimentation workflows

AI Data Pipeline Architecture

Serverless, event-driven data processing

PostgreSQL Source

Glue Crawler

Lambda Orchestrator

Macie Scanner

S3 Data Lake

Athena Queries

SageMaker / Analytics

40%

Faster ETL

1 TB+

Data Processed

20%

Cost Reduction

Impact

Reduced ETL runtime by 40% through optimized Glue jobs

Automated schema discovery and query validation

Enhanced compliance posture with Macie-based scans for 1 TB+ data

Reduced storage costs by 20% with lifecycle policies

Enabled secure AI experimentation workflows