Real-Time Event Processing with Pub/Sub and Dataflow

Process retail events in real time, validate incoming records, store valid events in BigQuery, and route bad events to a dead-letter table.
GCP • Streaming • Advanced • Retail

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What this project solves

Retail systems generate events throughout the day.

Orders are placed, payments succeed or fail, shipments move between locations, and delivery statuses keep changing. This data is useful only if it can be captured quickly, validated, and stored where teams can analyze it.

In this pipeline, you will process retail events in real time on GCP. You will publish order, payment, and shipment events to Pub/Sub, process them using Dataflow, store valid events in BigQuery, and route invalid or incomplete events to a dead-letter table for troubleshooting.

What you’ll build

  • Publish sample retail events to Pub/Sub
  • Process order, payment, and shipment streams
  • Validate incoming event data
  • Store valid events in BigQuery streaming tables
  • Send bad or incomplete events to a DLQ table
  • Track event timestamps and ingestion timestamps
  • Handle late or malformed events
  • Run streaming jobs using Dataflow

How you’ll build it

  • Create Pub/Sub topics for retail event streams
  • Publish sample order, payment, and shipment events
  • Build a Dataflow streaming job using Apache Beam
  • Validate events and split good records from bad records
  • Write valid events and DLQ records into BigQuery

Tools you’ll use

Pub/Sub • Dataflow • BigQuery • Apache Beam • Python

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Build streaming ingestion pipelines on GCP
  2. Publish event data into Pub/Sub
  3. Process streaming data using Dataflow
  4. Validate JSON events before loading
  5. Store valid events in BigQuery streaming tables
  6. Capture invalid records using a dead-letter table
  7. Handle late, malformed, and incomplete events
  8. Understand how streaming ingestion fits into a data platform

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