Streaming Data Pipelines and Real-Time Analytics

Modern organizations generate data every second, and waiting for batch processing is no longer an option for time-sensitive decisions. This course introduces you to the architecture and implementation of streaming systems designed to handle continuous, unbounded data flows efficiently. You will gain a solid foundation in how to architect pipelines that ingest, process, and store data in real-time. By the end of this course, you will be able to design systems that transform raw streams into actionable business intelligence as the data arrives. What you'll learn: - Understand the fundamental differences between batch and streaming data architectures - Configure Pub/Sub for high-throughput message ingestion and distribution - Build processing logic using Dataflow to transform and clean data on the fly - Apply windowing strategies to aggregate and analyze data over specific time intervals - Manage late-arriving data and ensure system reliability using watermarks and triggers - Store processed results in BigQuery for immediate analysis and reporting The course begins with essential terminology and core streaming theory before moving into the practical steps of configuring and deploying a functional data pipeline. You will read through detailed explanations of how these components interact to maintain high availability and low latency. This course is designed for beginners in data engineering or analytics who want to understand modern data movement; no previous experience with streaming tools is required. Start building your expertise in real-time data engineering today.