Ultimate BigQuery for Data Engineering

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.

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. Security, Governance, and Data Quality
11. BigQuery ML and Vertex AI
12. Real-Time Streaming Analytics
13. Enterprise Warehouse and ELT Project
14. The Future of BigQuery and Emerging Trends
Index

Dinak Lal is a data engineer specializing in cloud-native data platforms. With extensive experience building production BigQuery warehouses, dbt pipelines, and streaming analytics systems, he helps organizations transform raw data into insights. Dinak bridges the gap between SQL-focused analysts and infrastructure engineers.

About the Technical Reviewer
Akash Jatangi is a fintech product leader with deep experience building scalable credit, onboarding, and financial infrastructure products. He currently serves as a Senior Staff Product Manager at Intuit, where he works on the Intuit Business Credit Card and helps expand access to credit for small businesses through modern origination and onboarding systems.

Previously, Akash was the Director of Product at Deserve, where he led product strategy for credit card origination platforms powering multiple innovative consumer credit programs. Earlier in his career, Akash co-founded AutoVRse, an enterprise VR/AR company in India, where he led product vision, commercialization, and early enterprise growth. He holds an M.S. in Product Management from Carnegie Mellon University and dual degrees in Computer Science and Economics from BITS Pilani. Akash is passionate about fintech, product strategy, data-driven systems, and practical technology adoption.

Madhu Agrahara Gopalakrishna is a Senior Principal Engineer based in Cupertino, California, with more than 20 years of experience in secure storage architecture, filesystem security, distributed systems, and embedded multimedia. He currently leads enterprise security initiatives at Dell Technologies. His areas of focus include Data-at-Rest and Over-the- Wire encryption, TLS 1.3 modernization, FIPS compliance, post-quantum cryptography, key management integration, anomaly detection, and cybersecurity.

Madhu is the inventor of 10 U.S. patents in filesystem security that have been incorporated into globally deployed enterprise storage platforms. His expertise spans cryptography, clustered systems, Kubernetes, and low-level software development using C, C++, Python, and Go. He holds a Bachelor of Engineering degree in Electronics and Communication from Dr. A.I.T., Bengaluru, India.