Название: Ultimate BigQuery for Data Engineering: Master BigQuery, Data Modeling, dbt, Dataflow, Apache Beam, Streaming Analytics, BigQuery ML, and Production Data Engineering
Автор: Dinak Lal
Издательство: Orange Education Pvt Ltd, AVA
Год: 2026
Страниц: 489
Язык: английский
Формат: epub (true)
Размер: 28.9 MB

Transform Data into Intelligence at Cloud Scale.

Key Features:
- Get a free one-month digital subscription to 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 Engineering takes 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.

Contents: