Description

Introduction

Google BigQuery is a fully managed and serverless cloud data warehouse from Google Cloud. It is built for fast SQL-based analytics on very large datasets.It separates storage and compute. Because of this design, users can scale resources based on workload needs. In addition, it offers features such as query optimization, caching, and partitioning. Moreover, it integrates with other Google Cloud services.

As a result, BigQuery supports real-time analytics, business intelligence, and machine learning workloads. Therefore, understanding its performance and cost structure is essential for building efficient data solutions.

Learner Prerequisites

• Basic understanding of SQL (SELECT, JOIN, GROUP BY)
• Familiarity with data warehousing concepts
• Awareness of cloud computing fundamentals
• Basic knowledge of analytics or reporting systems
• Understanding of datasets, tables, and schemas

Table of Contents

1. Introduction to BigQuery Performance & Cost Landscape
1.1 Overview of BigQuery architecture and execution model
1.2 Understanding on-demand vs flat-rate pricing
1.3 How query processing impacts performance and cost
1.4 Key factors influencing slot usage and billing
1.5 Common causes of high-cost queries

2. Query Execution & Slot Utilization in BigQuery
2.1 Understanding query execution stages
2.2 Role of slots in query processing
2.3 Parallelism and distributed processing model
2.4 Identifying slot contention and bottlenecks
2.5 Optimizing slot allocation for efficiency

3. Data Modeling for Performance Optimization
3.1 Choosing between normalized and denormalized models
3.2 Star and snowflake schema impact on performance
3.3 Importance of schema design in query efficiency
3.4 Handling large datasets with optimized structures
3.5 Reducing data scan through effective modeling

4. Partitioning & Clustering Optimization Techniques
4.1 Table partitioning strategies and use cases
4.2 Time-based and ingestion-time partitioning
4.3 Clustering keys and their performance benefits
4.4 Combining partitioning and clustering effectively
4.5 Avoiding partition scan inefficiencies

5. SQL Query Optimization Techniques
5.1 Reducing data scanned using SELECT best practices
5.2 Optimizing JOIN operations for large datasets
5.3 Using filters and predicates efficiently
5.4 Avoiding unnecessary subqueries and transformations
5.5 Leveraging cached results and query reuse

6. Monitoring, Logging & Cost Control Strategies
6.1 Using query execution plans for analysis
6.2 Monitoring via BigQuery job history and logs
6.3 Setting up cost controls and quotas
6.4 Using audit logs for performance tracking
6.5 Identifying and optimizing expensive queries

Conclusion

This training provides a practical understanding of performance tuning in Google BigQuery. It focuses on both efficiency and cost control.In addition, learners explore query execution, slot usage, and data modeling strategies. They also learn how to reduce unnecessary processing. Moreover, the course explains partitioning, clustering, and SQL optimization techniques.

As a result, participants can improve query performance significantly. Therefore, they will be able to build scalable and cost-efficient data solutions on Google Cloud.