Event Driven Data Pipeline Design Architecture On AWS | Part-1/100 #shorts
Why It Matters
It demonstrates a scalable, serverless pipeline that turns raw CSV drops into analytics‑ready data, a blueprint essential for modern data‑engineering teams and interview preparation.
Key Takeaways
- •Use EventBridge to capture S3 file‑arrival events in real time.
- •Queue events in SQS as a buffer for multiple CSV uploads.
- •Trigger Lambda from SQS; route failures to a dead‑letter queue.
- •Lambda initiates Glue workflow; metadata stored in DynamoDB for tracking.
- •CloudWatch monitors Glue jobs; SNS alerts send notifications to Slack.
Summary
The video walks through a full‑stack, event‑driven data pipeline built on AWS, a frequent topic in data‑engineering interviews. It starts with external systems dropping CSV files into an S3 bucket, where an EventBridge rule detects the arrival, applies optional filters, and forwards the event to an SQS queue that acts as a buffer for high‑volume uploads.
The queued messages trigger a Lambda function, which extracts file metadata and launches a Glue workflow. Glue performs the heavy lifting—transforming the CSV data, loading it into a staging table, and finally merging it into a Redshift fact table. Throughout the process, file‑level metadata is persisted in DynamoDB to maintain idempotency and track processing status. Failed events are routed to a dead‑letter queue for later reprocessing.
The presenter highlights operational safeguards: CloudWatch monitors the Glue jobs, and any failure generates an SNS notification that fans out to Slack channels for rapid response. He also offers a detailed PDF of best practices, inviting viewers to request it via comment.
By chaining managed services—EventBridge, SQS, Lambda, Glue, DynamoDB, Redshift, CloudWatch, and SNS—the architecture achieves scalability, decoupling, and real‑time responsiveness, illustrating a production‑grade pattern that interviewers expect candidates to understand.
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