Ultimate BigQuery for Data Engineering | 112.98 MB
Title: Ultimate BigQuery for Data Engineering
Author: Dinak Lal
Category: Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Database Management
Language: English | 485 Pages | ISBN: 9788169646291
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!
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!
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