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Google Cloud : Building Resilient Streaming Analytics Systems on Google Cloud

  • Learned to design resilient, end-to-end streaming data pipelines that address real-time business needs on Google Cloud Platform (GCP)

  • Managed and processed unbounded, continuous data streams, applying techniques to handle late data, watermarks, and windowing

  • Developed hands-on skills in ingesting, transforming, and storing streaming data for both low-latency operational analytics and high-throughput data warehousing

  • Understood how to integrate serverless messaging, stream processing, and data storage services to create a cohesive, real-time analytics architecture

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Tools & Techniques:

  • Pub/Sub – serverless messaging for event ingestion, distribution, and resilient event management (Push vs. Pull)

  • Dataflow (Apache Beam) – unified stream and batch processing, including windowing and triggers

  • BigQuery – real-time data warehousing and advanced querying (analytic window functions, GIS functions)

  • Bigtable – high-throughput, low-latency NoSQL storage optimized for streaming workloads

  • SQL & performance tuning – query optimization and cost management in BigQuery

  • Real-time data pipeline design – transformation, enrichment, and orchestration

  • Google Cloud Skills Boost / Qwiklabs – hands-on labs for practical implementation in the GCP environment

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