Building Resilient Streaming Analytics Systems on Google Cloud Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

A well-structured, lab-centric introduction to building resilient streaming analytics systems on Google Cloud. This course guides learners through the core components of real-time data processing with hands-on labs using GCP services. You'll progress from understanding streaming fundamentals to implementing and optimizing end-to-end pipelines using Pub/Sub, Dataflow, BigQuery, and Bigtable. With approximately 8 hours of content and practical exercises, this course is ideal for data professionals looking to expand their analytics capabilities into real-time use cases.

Module 1: Course Introduction

Estimated time: 0.1 hours

Course goals and structure
Overview of streaming analytics on Google Cloud
Learning outcomes preview

Module 2: Streaming Data Challenges

Estimated time: 0.2 hours

Understanding real-time data demands
Challenges of high data volume and velocity
Latency requirements in streaming systems
Use cases for stream processing

Module 3: Pub/Sub Messaging

Estimated time: 1.2 hours

Pub/Sub fundamentals and architecture
Push vs pull messaging patterns
Publishing messages to Pub/Sub
Lab: Publish streaming data into Pub/Sub

Module 4: Dataflow Streaming

Estimated time: 1.5 hours

Introduction to Dataflow and stream pipelines
Windowing in streaming data
Transformations and aggregations in Dataflow
Lab: Build a Dataflow streaming pipeline

Module 5: BigQuery & Bigtable Streaming

Estimated time: 4 hours

Streaming data into BigQuery for analytics
High-throughput ingestion into Bigtable
Building dashboards from streaming data
Labs: Analytics with BigQuery and Bigtable workloads

Module 6: BigQuery Advanced Features

Estimated time: 1 hour

Window functions and advanced SQL
GIS extensions in BigQuery
Query performance optimization
Cost efficiency strategies
Lab: Optimize BigQuery queries

Module 7: Course Recap