Description

Introduction:

Google BigQuery is a fully-managed, serverless data warehouse that enables fast SQL queries using the processing power of Google’s infrastructure. This training dives deep into advanced SQL features in BigQuery, equipping participants to write optimized queries, handle complex data structures, and perform large-scale analytics. Designed for professionals aiming to scale their data capabilities, this course blends theory with hands-on labs and real-world use cases.

Prerequisites:

• Basic knowledge of SQL

• Familiarity with Google Cloud Platform (GCP) basics

• Understanding of data warehousing concepts

• Experience with any RDBMS (like MySQL, PostgreSQL, etc.) is recommended

Table of Contents:

1. Introduction to BigQuery

• 1.1 Overview of BigQuery architecture

• 1.2 Key features and advantages

• 1.3 Dataset, table, and schema concepts

2. Intermediate SQL Recap

• 2.1 JOINs, Subqueries, and Aggregations

• 2.2 Window functions basics

• 2.3 Data filtering and transformations

3. Advanced SQL Techniques in BigQuery

• 3.1 Advanced window functions and analytics functions

• 3.2 ARRAY and STRUCT data types

• 3.3 Working with nested and repeated fields

4. Performance Optimization

• 4.1 Best practices for query optimization

• 4.2 Partitioned and clustered tables

• 4.3 Query plan analysis and execution metrics

5. User-Defined Functions (UDFs) and Stored Procedures

• 5.1 Writing and using UDFs

• 5.2 Creating stored procedures

• 5.3 Reusability and modular query design

6. BigQuery ML & Analytics

• 6.1 Introduction to BigQuery ML

• 6.2 Building and training simple models using SQL

• 6.3 Model evaluation and predictions

7. Real-Time and Federated Queries

• 7.1 Streaming data ingestion

• 7.2 Querying external data sources (e.g., Cloud Storage, Sheets)

• 7.3 Data lake vs. warehouse scenarios

8. Security and Access Controls

• 8.1 Dataset and table-level permissions

• 8.2 Row-level and column-level security

• 8.3 Auditing and monitoring usage

9. Hands-on Labs and Case Studies

• 9.1 Real-world analytics project using public datasets

• 9.2 Troubleshooting and optimization exercises

• 9.3 Interactive query tuning session

By the end of this course, participants will be confident in writing high-performance, production-grade SQL queries in BigQuery. With advanced skills in handling complex data structures and optimizing analytical workloads, they will be well-equipped to support data-driven decision-making at scale across industries.