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Case study

Real-time Data Pipeline

Data platform engineering · GCP

GCP Pub/SubDataflowCloud SQLPythonGCP Operations Suite

Impact

High-throughput ingestion, zero data loss

Overview

Built and operated real-time data pipelines on GCP using Pub/Sub and Dataflow, enabling high-throughput stream ingestion and processing integrated with Cloud SQL and backend services.

The story

Streaming data at volume across distributed systems, where a pipeline failure means lost data or broken downstream services — so reliability and observability had to be built in from the start.

Role

Pipeline engineering, GCP data services, distributed systems

The challenge

Keeping high-throughput ingestion and processing reliable across distributed systems while integrating cleanly with Cloud SQL and multiple backend services.

Approach

1

Built and maintained Pub/Sub topics and Dataflow jobs for real-time stream processing.

2

Integrated pipelines with Cloud SQL and backend services to ensure consistent data flow.

3

Implemented monitoring and alerting with GCP Operations Suite to catch and resolve issues fast.

Outcome

Reliable real-time data processing pipeline handling high-volume streams with integrated monitoring and fast incident resolution.

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