Description

Introduction

Google BigQuery is a fully managed and serverless cloud data warehouse from Google Cloud. It is designed for fast analytics on large datasets using standard SQL.It enables users to run queries quickly without managing infrastructure. In addition, it supports real-time data processing and scalable performance. Moreover, it integrates well with analytics and BI tools.

Because of these capabilities, it is widely used in data engineering and business intelligence. Therefore, this training focuses on SQL fundamentals tailored for BigQuery environments.

Learner Prerequisites

• Basic understanding of relational databases
• Familiarity with core SQL concepts (SELECT, FROM, WHERE)
• Knowledge of tables, rows, and columns
• Basic analytical and logical reasoning skills
• No prior BigQuery experience required

Table of Contents

1. Introduction to SQL in BigQuery Environment
1.1 Overview of SQL in cloud data platforms
1.2 BigQuery architecture and serverless processing model
1.3 Understanding datasets, tables, and schemas
1.4 Navigating the BigQuery console and interface
1.5 Writing and executing your first SQL query

2. Data Retrieval Techniques & Filtering in SQL
2.1 SELECT statement and column selection strategies
2.2 WHERE clause with conditions and logical operators
2.3 Pattern matching using LIKE, IN, BETWEEN
2.4 Sorting data using ORDER BY and LIMIT
2.5 Handling NULL values in queries

3. SQL Joins and Multi-Table Data Operations
3.1 Understanding relational joins in BigQuery
3.2 INNER JOIN vs LEFT JOIN vs RIGHT JOIN vs FULL JOIN
3.3 Joining multiple tables for complex datasets
3.4 Self joins and use cases
3.5 Resolving join conflicts and duplicate records

4. Aggregations, Grouping & Analytical SQL
4.1 Aggregate functions (SUM, AVG, COUNT, MIN, MAX)
4.2 GROUP BY clause for categorized analysis
4.3 HAVING clause for filtered aggregations
4.4 Using CASE statements for conditional aggregation
4.5 Advanced grouping strategies for business insights

5. Subqueries, Nested Queries & Set Operations
5.1 Writing single-row and multi-row subqueries
5.2 Correlated subqueries in BigQuery
5.3 Using EXISTS and NOT EXISTS conditions
5.4 UNION, UNION ALL, INTERSECT, and EXCEPT operations
5.5 Simplifying complex queries using nesting techniques

6. Advanced SQL Functions in BigQuery
6.1 String functions for data transformation
6.2 Date and time functions for time-series analysis
6.3 Mathematical functions for numerical operations
6.4 Conditional logic using IF and CASE expressions
6.5 Type conversion and data formatting functions

7. Query Optimization & Performance Best Practices
7.1 Understanding query execution and cost in BigQuery
7.2 Reducing data scanned using filters and partitions
7.3 Using clustered and partitioned tables effectively
7.4 Avoiding inefficient joins and subqueries
7.5 Best practices for writing scalable SQL queries

8. Real-World SQL Use Cases in BigQuery
8.1 Business reporting and dashboard data preparation
8.2 Customer segmentation and sales analysis
8.3 Log and event data analysis
8.4 Data cleaning and transformation workflows
8.5 Case study: End-to-end SQL analytics pipeline

Conclusion

This training provides a solid foundation in SQL using Google BigQuery. It covers both basic queries and advanced analytical techniques.In addition, learners develop skills in joins, aggregations, and subqueries. They also learn how to improve query performance. Moreover, real-world examples help in practical understanding.

As a result, participants can work efficiently with large datasets. Therefore, they will be able to build reliable and scalable SQL workflows for modern data environments.