Event Streams → BigQuery

GCP • Streaming • Advanced • Retail

Architecture Diagram

Overview

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 and stored in a place where teams can analyze it.

In this pipeline, you will build a streaming ingestion pipeline on GCP.

You will publish order, payment, and shipment events to Pub/Sub, process them using Dataflow, store valid events in BigQuery, and send invalid or incomplete records to a dead-letter table for troubleshooting.

What You Will 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

Tech Stack

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

Learning Outcomes

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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