Building Streaming Data Pipelines on GCP

Modern businesses rely on split-second insights, making the ability to process continuous streams of data a critical skill for data professionals. This text-based course guides you through the foundational concepts and practical architectures needed to handle unbounded data streams effectively. You will transition from batch-processing mindsets to streaming architectures, learning how to ingest, transform, and store real-time data. By understanding windowing, triggers, and late-arriving data, you will gain the skills to build resilient pipelines that power live dashboards and instant analytics. What you'll learn: - Understand core streaming concepts, including bounded versus unbounded datasets and event-time versus processing-time. - Configure Pub/Sub topics and subscriptions to ingest high-velocity data feeds reliably. - Build streaming pipelines using Dataflow to filter, aggregate, and enrich data on the fly. - Apply windowing strategies and handle late data accumulation for accurate time-based analysis. - Stream processed data directly into BigQuery for real-time querying and operational analytics. - Implement pipeline observability, monitoring, and error-handling strategies to maintain system health. The course begins with essential streaming terminology and architectural patterns before moving into step-by-step written guides for building and connecting ingestion, processing, and storage layers. You will finish by exploring monitoring techniques to keep your real-time pipelines running smoothly. This course is designed for beginner data engineers, developers, and analysts who want to transition into real-time data processing. No prior experience with streaming analytics or cloud data pipelines is required. Start reading today to master the foundations of real-time data streaming.