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Serverless Big Data Processing Pipeline on GCP

Design a cost-effective serverless pipeline to process 10TB+ daily data with real-time analytics capabilities.

Our role: Cloud Data Architect

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The challenge

Traditional VM-based processing was expensive and couldn't scale dynamically. Needed serverless architecture with pay-per-use model.

What Devs & Logics delivered

  • Solution 1: Cloud Functions for event-driven data ingestion from multiple sources
  • Solution 2: Pub/Sub for reliable message queuing and event streaming
  • Solution 3: BigQuery for data warehousing and SQL analytics
  • Solution 4: Dataflow (Apache Beam) for ETL transformations
  • Solution 5: Cloud Storage for data lake with lifecycle policies
  • Solution 6: Looker for real-time dashboards and business intelligence

Outcomes

  • Reduced infrastructure costs by 70% compared to VM-based solution
  • Processing time reduced from 12 hours to 2 hours
  • Real-time analytics enabled for business decision making

Technologies

GCPCloud FunctionsPub/SubBigQueryDataflowCloud StorageLookerTerraformCloud Composer
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