Batch ETL Data Lake Pipeline with Glue and Athena — AWS architecture diagram

About This Architecture

Batch ETL data lake pipeline orchestrating daily ingestion, transformation, and analytics using AWS Glue, S3, and Athena. EventBridge triggers a scheduled Glue ETL job that validates and cleanses raw JSONL logs from S3 Raw Bucket, writes cleaned Parquet to S3 Processed Bucket, and catalogs schema in Glue Data Catalog. CloudWatch monitors job execution and alerts, while least-privilege IAM roles isolate Glue and Athena permissions. Athena queries the curated data for daily reporting, serving analytics to BI users with read-only access to processed tiers. This architecture demonstrates medallion lakehouse patterns, cost-efficient Parquet storage, and audit-trail immutability of raw data. Fork and customize this diagram on Diagrams.so to adapt scheduling, add quality gates, or integrate with your data governance framework.

People also ask

How do I build a batch ETL data lake pipeline on AWS with Glue and Athena?

This diagram shows a medallion lakehouse pattern: EventBridge triggers daily Glue ETL jobs that ingest raw JSONL, validate and clean it, write Parquet to S3 Processed Bucket, and register schema in Glue Data Catalog. Athena queries the curated data for BI reporting, with least-privilege IAM roles and CloudWatch monitoring ensuring security and observability.

Batch ETL Data Lake Pipeline with Glue and Athena

AWSintermediatedata-engineeringETLdata-lakeGlueAthena
Domain: Data EngineeringAudience: Data engineers building batch ETL pipelines on AWS
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Created by

August 10, 2026

Updated

August 11, 2026 at 6:11 AM

Type

data pipeline

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