Data Warehousing with BigQuery: Storage Design, Query Optimisation, and Administration

Looking to learn everything you need to know about the internal workings of BigQuery? This course covers best practices for designing, optimizing, and administering your data warehouse.

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3 day course
Partner of the Year
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

On this three-day course, you’ll master BigQuery architecture and discover how to design optimal storage and schemas for data ingestion and changes.

Jellyfish has recently been named a Google Cloud Partner. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed. All of our trainers are experienced practitioners, so you can learn with total confidence.

Through a combination of lectures, demos, and labs, you’ll cover techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools.

You’ll also learn about the different pricing models. Finally, you’ll go over various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.

Our Data Warehousing with BigQuery: Storage Design, Query Optimisation and Administration course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the US.

Course overview

Who should attend:

This course is ideal for data analysts, data scientists, data engineers, and developers who perform work on a scale that requires advanced BigQuery internal knowledge to optimize performance.

What you'll learn:

By the end of this course, you will be able to:

  • 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


To get the most out of this course, you should have completed the Google Cloud Fundamentals: Big Data and Machine Learning course.

Course agenda

Module 1: BigQuery Architecture Fundamentals
  • Introduction
  • BigQuery Core Infrastructure
  • BigQuery storage
  • BigQuery query processing
  • BigQuery data shuffling
Module 2: Storage & Schema Optimizations
  • BigQuery storage
  • Partitioning and clustering
  • Nested and repeated fields
  • ARRAY and STRUCT syntax
  • Best practices
Module 3: Ingesting Data
  • Data ingestion options
  • Batch ingestion
  • Streaming ingestion
  • Legacy streaming API
  • BigQuery storage write API
  • Query materialization
  • Query external data sources
  • Data transfer service
Module 4: Changing Data
  • Managing change in data warehouses
  • Handling slowly changing dimensions (SCD)
  • DML statements
  • DML best practices and common issues
Module 5: Improving Read Performance
  • BigQuery's cache
  • Materialized views
  • BI engine
  • High throughput reads
  • BigQuery storage read API
Module 6: Optimizing & Troubleshooting Queries
  • Simple query execution
  • SELECTs and aggregation
  • JOINs and skewed JOINs
  • Filtering and ordering
  • Best practices for functions
Module 7: Workload Management & Pricing
  • BigQuery slots
  • Pricing models and estimates
  • Slot reservations
  • Controlling costs
Module 8: Logging & Monitoring
  • Cloud monitoring
  • BigQuery admin panel
  • Cloud audit logs
  • Query path and common errors
Module 9: Security in BigQuery
  • Secure resources with IAM
  • Authorized views
  • Secure data with classification
  • Encryption
  • Data discovery and governance
Module 10: Automating Workloads
  • Scheduling queries
  • Scripting
  • Stored procedures
  • Integration with Big Data products
Module 11: Machine Learning in BigQuery
  • 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 model with BigQuery ML
  • BigQuery ML explainability
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