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Data Engineering on GCP (Part 4): Building a Production-Ready Real-Time Streaming Pipeline

Build a resilient GCP streaming pipeline with Pub/Sub, Apache Beam on Dataflow, raw-event landing, quarantine handling, event-time windows, and BigQuery Storage Write API sinks.

Google Cloud
Data Engineering on GCP (Part 4): Building a Production-Ready Real-Time Streaming Pipeline
Key Architecture Decisions & Takeaways
  • Direct BigQuery subscriptions vs Apache Beam worker pool tradeoffs
  • Decoupled ingestion with Transactional Outbox pattern & Dead-Letter Queues
  • Cost & latency benchmarks: Storage Write API vs legacy streaming sinks
Tharun Vempati
Tharun Vempati · Oct 07, 2026
8 min read

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Tharun Vempati

Written by Tharun Vempati

GCP Professional Cloud Architect · CKA · Red Hat EX280

Practitioner specializing in distributed backend systems, Kubernetes orchestration, and cloud infrastructure delivery. GCloud Cafe is an independent engineering journal documenting reproducible architectures, real lab experiments, and production lessons without corporate fluff or AI slop.

GCP PCA CNCF CKA Red Hat EX280 Read Author Bio →
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