About This Architecture

End-to-end AWS data pipeline architecture ingesting user events and payments through Kinesis and SQS, processing with Lambda, ECS Fargate, and Glue ETL, then storing across S3 Data Lake, Redshift, RDS, and ElastiCache. Data flows from Next.js frontend through API Gateway and Strapi/Laravel APIs, with real-time streaming to Lambda Event Processor and batch transformations via Glue into curated Parquet and Redshift aggregates. Serving layer exposes data via Athena for ad-hoc queries and QuickSight for BI dashboards, while CloudFront delivers frontend content and CloudWatch monitors pipeline health. This architecture demonstrates separation of concerns—streaming ingestion, batch processing, transactional storage, and analytics serving—enabling scalable, cost-efficient data operations. Fork this diagram on Diagrams.so to customize data sources, add additional processing stages, or integrate alternative AWS services like Kinesis Firehose or EventBridge. The design includes payment orchestration through Step Functions and external providers, showing how to handle both analytical and operational data flows in a unified platform.

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How do I design an end-to-end AWS data pipeline that handles both real-time events and batch analytics?

This diagram shows a complete AWS data pipeline separating ingestion (Kinesis, SQS), processing (Lambda, ECS Fargate, Glue ETL), storage (S3 Data Lake, Redshift, RDS), and serving (Athena, QuickSight) layers. Real-time user events stream through Kinesis to Lambda, while batch jobs transform raw S3 data into curated Parquet and Redshift aggregates for analytics dashboards.