GCP200DWBQ
Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
In this course, you learn about the internals of BigQuery and best practices for designing, optimizing, and administering your data warehouse. Through a combination of lectures, demos, and labs, you learn about BigQuery architecture and how to design optimal storage and schemas for data ingestion and changes. Next, you learn techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools. You also learn about the different pricing models. Finally, you learn various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.
What you will learn
- Describe BigQuery architecture fundamentals.
- Implement storage and schema design patterns to improve performance.
- Use DML and schedule data transfers to ingest data.
- Apply best practices to improve read efficiency and optimize query performance.
- Manage capacity and automate workloads.
- Understand patterns versus anti-patterns to optimize queries and improve read performance.
- Use logging and monitoring tools to understand and optimize usage patterns.
- Apply security best practices to govern data and resources.
- Build and deploy several categories of machine learning models with BigQuery ML.
Prerequisites
- Introduction to Data Engineering
Target audience
- Data analysts, data scientists, data engineers, and developers who perform work on a scale that requires advanced BigQuery internals knowledge to optimize performance.
Training Program
11 modules to master the fundamentals
Objectives
- Explain the benefits of columnar storage.
- Understand how BigQuery processes data.
- Explore the basics of BigQuery's shuffling service to improve query efficiency.
Topics covered
- →Introduction
- →BigQuery Core Infrastructure
- →BigQuery Storage
- →BigQuery Query Processing
- →BigQuery Data Shuffling
Activities
Labs and demos
Objectives
- Compare the performance of different schemas (snowflake, denormalized, and nested and repeated fields).
- Partition and cluster data for better performance.
- Improve schema design using nested and repeated fields.
- Describe additional best practices such as table and partition expiration
Topics covered
- →BigQuery Storage
- →Partitioning and Clustering
- →Nested and Repeated Fields
- →ARRAY and STRUCT syntax
- →Best Practices
Activities
Labs and demos
Objectives
- Ingest batch and streaming data.
- Query external data sources.
- Schedule data transfers.
- Understand how to use Storage Write API.
Topics covered
- →Data Ingestion Options
- →Batch Ingestion
- →Streaming Ingestion
- →Legacy Streaming API
- →BigQuery Storage Write API
- →Query Materialization
- →Query External Data Sources
- →Data Transfer Service
Activities
Labs and demos
Objectives
- Write DML statements.
- Address common DML performance problems and bottlenecks.
- Identify slowly changing dimensions (SCD) in your data and make updates.
Topics covered
- →Managing Change in Data Warehouses
- →Handling Slowly Changing Dimensions (SCD)
- →DML statements
- →DML Best Practices and Common Issues
Objectives
- Explore BigQuery's cache.
- Create materialized views.
- Work with BI Engine to accelerate your SQL queries.
- Use the Storage Read API for fast access to BigQuery-managed storage.
- Explain the caveats of using external data sources.
Topics covered
- →BigQuery's Cache
- →Materialized Views
- →BI Engine
- →High Throughput Reads
- →BigQuery Storage Read API
Activities
Labs and demos
Objectives
- Interpret BigQuery execution details and the query plan.
- Optimize query performance by using suggested methods for SQL statements and clauses.
- Demonstrate best practices for functions in business use cases.
Topics covered
- →Simple Query Execution
- →SELECTs and Aggregation
- →JOINs and Skewed JOINs
- →Filtering and Ordering
- →Best Practices for Functions
Activities
Labs and demos
Objectives
- Define a BigQuery slot.
- Explain pricing models and pricing estimations (BigQuery UI, bq dry_run, jobs API).
- Understand slot reservations, commitments, and assignments.
- Identify best practices to control costs.
Topics covered
- →BigQuery Slots
- →Pricing Models and Estimates
- →Slot Reservations
- →Controlling Costs
Activities
Demos
Objectives
- Use Cloud Monitoring to view BigQuery metrics.
- Explore the BigQuery admin panel.
- Use Cloud Audit logs.
- Work with INFORMATION_SCHEMA tables to get insights for your BigQuery entities.
Topics covered
- →Cloud Monitoring
- →BigQuery Admin Panel
- →Cloud Audit Logs
- →INFORMATION_SCHEMA
- →Query Path and Common Errors
Activities
Labs and demos
Objectives
- Explore data discovery using Data Catalog.
- Discuss data governance using DLP API and Data Catalog.
- Create IAM policies (e.g., authorized views) to secure resources.
- Secure data with classifications (e.g., row-level policies).
- Understand how BigQuery uses encryption.
Topics covered
- →Secure Resources with IAM
- →Authorized Views
- →Secure Data with Classification
- →Encryption
- →Data Discovery and Governance
Activities
Labs and demos
Objectives
- Schedule queries.
- Use scripting and stored procedures to build custom transformations.
- Describe how to integrate BigQuery workloads with other Google Cloud big data products.
Topics covered
- →Scheduling Queries
- →Scripting
- →Stored Procedures
- →Integration with Big Data Products
Activities
Demos
Objectives
- Describe some of the different applications of BigQuery ML.
- Build and deploy several categories of machine learning models with BigQuery ML.
- Use AutoML Tables to solve high-value business problems.
Topics covered
- →Introduction to BigQuery ML
- →How to Make Predictions with BigQuery ML
- →How to Build and Deploy a Recommendation System with BigQuery ML
- →How to Build and Deploy a Demand Forecasting Solution with BigQuery ML
- →Time-Series Models with BigQuery ML
- →BigQuery ML Explainability
Activities
Labs and demos
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
February 18, 2026
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May 20, 2026
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August 17, 2026
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November 16, 2026
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January 7, 2026
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April 1, 2026
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July 1, 2026
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October 5, 2026
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April 1, 2026
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July 1, 2026
Distanciel • Français
October 5, 2026
Distanciel • Français
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote