Ultimate BigQuery for Data Engineering: Master BigQuery, Data Modeling, dbt, Dataflow, Apache Beam, Streaming Analytics, BigQuery ML, and Production Data Engineering

Transform Data into Intelligence at Cloud Scale.

Key Features
● Get a free one-month digital subscription to http://www.avaskillshelf.com.
● Production-grade BigQuery warehouse engineering covering data modeling, partitioning, clustering, and cost optimization.
● End-to-end pipeline engineering with dbt, Apache Beam, Dataflow, Cloud Composer, and CI/CD automation.
● Two complete capstone projects — a real-time streaming analytics system, and a full dbt as well as BigQuery ELT platform.

Book Description

Every Great AI System Begins with a Great Data Platform

BigQuery is the backbone of modern cloud data engineering. Ultimate BigQuery for Data Engineeringtakes you from SQL fundamentals to building a complete production analytics platform on Google Cloud — with hands-on labs and real engineering patterns at every stage.

You begin with BigQuery internals — columnar storage, Dremel execution, and slot management — then advance through data modeling, partitioning, clustering, cost engineering, and ELT pipelines with dbt. The book covers Apache Beam, Dataflow, Cloud Composer, and streaming analytics with Pub/Sub, before addressing enterprise governance, including row-level security, column masking, data quality testing, CI/CD automation, and ML with BigQuery ML as well as Vertex AI.

The final three chapters deliver two complete capstone projects — a real-time streaming analytics system and a full warehouse ELT platform built with dbt and BigQuery — before closing with the future of BigQuery and emerging trends that will shape the next generation of cloud data engineering. Thus, by the end, you will have a portfolio of production-grade projects that prove your skills on Google Cloud!

What you will learn
● Master BigQuery internals including columnar storage, Dremel execution, and slot management.
● Design scalable data models using partitioning, clustering, and physical optimization strategies.
● Build production-grade ELT pipelines using dbt transformation layers and automated CI/CD workflows.
● Implement streaming analytics using Apache Beam, Dataflow, Pub/Sub, and Cloud Composer orchestration.
● Secure data warehouses using row-level security policies, column masking, and data quality testing.
● Deploy ML models and predictions using BigQuery ML and Vertex AI on Google Cloud.

Table of Contents
1. Data Engineering Basics and BigQuery’s Role
2. Inside BigQuery Storage, Compute, and Execution
3. Getting Data into BigQuery
4. Modeling for Scale
5. Partitioning, Clustering, and Optimization
6. Performance and Cost Engineering
7. ELT with dbt and BigQuery
8. Apache Beam and Dataflow
9. Spark and BigQuery Integration
10. Orchestration with Cloud Composer
11. Security, Governance, and Data Quality
12. BigQuery ML and Vertex AI
13. Real-Time Streaming Analytics
14. Enterprise Warehouse and ELT Project
15. The Future of BigQuery and Emerging Trends
Index