Data Engineering, Big Data, and Machine Learning on GCP Specialization Course Syllabus

Overview: This specialization offers a comprehensive, hands-on learning path focused on data engineering, big data processing, and machine learning on Google Cloud Platform (GCP). Over approximately 44 hours, learners progress from foundational concepts to building and deploying production-grade data and ML pipelines. Through structured modules and guided labs, you'll gain practical experience with core GCP services including BigQuery, Dataflow, Pub/Sub, Dataproc, and Vertex AI. The course blends theoretical knowledge with real-world implementation, culminating in a final project that demonstrates end-to-end system design. Ideal for professionals targeting Google Cloud certifications in data engineering or machine learning.

Module 1: Google Cloud Big Data and Machine Learning Fundamentals

Estimated time: 5 hours

• Introduction to the GCP data-to-AI lifecycle
• Overview of key services: BigQuery, Dataflow, Pub/Sub, Dataproc, and Vertex AI
• Understanding use cases for big data and machine learning on GCP
• Hands-on lab: Interact with Pub/Sub, Dataflow, and BigQuery through real cloud environments

Module 2: Modernizing Data Lakes and Data Warehouses with Google Cloud

Estimated time: 8 hours

• Understanding differences between data lakes and data warehouses
• Design patterns using Cloud Storage, BigQuery, and Dataproc
• Role and responsibilities of a data engineer in modern architectures
• Hands-on lab: Load and transform data in BigQuery and Dataproc using real datasets

Module 3: Building Batch Data Pipelines on Google Cloud

Estimated time: 17 hours

• Differentiating between batch ETL and ELT patterns
• Using Apache Hadoop and Spark on Dataproc
• Building and managing Dataflow pipelines
• Orchestrating workflows with Cloud Composer and Data Fusion

Module 4: Building Resilient Streaming Analytics Systems on Google Cloud

Estimated time: 8 hours

• Real-time streaming use cases and requirements
• Implementing Pub/Sub messaging pipelines
• Streaming data processing with Dataflow, including windowing and transformations
• Integrating streaming pipelines with BigQuery for real-time analytics

Module 5: Smart Analytics, Machine Learning, and AI on Google Cloud

Estimated time: 6 hours

• Distinguishing between ML, AI, and deep learning
• Using unstructured data APIs (e.g., Vision, Natural Language)
• Training models with BigQuery ML and Vertex AI AutoML
• Building predictive analytics using Jupyter notebooks

Module 6: Final Project

Estimated time: 10 hours

• Design and implement an end-to-end data pipeline on GCP
• Incorporate batch and streaming components using Pub/Sub, Dataflow, and BigQuery
• Build and deploy a machine learning model using Vertex AI with model monitoring

Prerequisites

• Familiarity with Linux command line
• Basic knowledge of Python programming
• Understanding of SQL and data querying fundamentals

What You'll Be Able to Do After

• Design and operationalize batch and streaming data pipelines on GCP
• Modernize data lakes and warehouses using scalable GCP services
• Apply machine learning to real datasets using AutoML, BigQuery ML, and Vertex AI
• Build production-ready ML pipelines with feature stores and model monitoring
• Prepare effectively for Google Professional Data Engineer or Machine Learning Engineer certifications