3 day course 
Partner of the Year 
Private Course Credits
Select the pre-paid training investment that’s right for you and help your money stretch a little further with our course credits.
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
Prerequisites
To get the most out of this course, you should have completed the Google Cloud Fundamentals: Big Data and Machine Learning course.
Course agenda
- Introduction
- BigQuery Core Infrastructure
- BigQuery storage
- BigQuery query processing
- BigQuery data shuffling
- BigQuery storage
- Partitioning and clustering
- Nested and repeated fields
- ARRAY and STRUCT syntax
- Best practices
- Data ingestion options
- Batch ingestion
- Streaming ingestion
- Legacy streaming API
- BigQuery storage write API
- Query materialization
- Query external data sources
- Data transfer service
- Managing change in data warehouses
- Handling slowly changing dimensions (SCD)
- DML statements
- DML best practices and common issues
- BigQuery's cache
- Materialized views
- BI engine
- High throughput reads
- BigQuery storage read API
- Simple query execution
- SELECTs and aggregation
- JOINs and skewed JOINs
- Filtering and ordering
- Best practices for functions
- BigQuery slots
- Pricing models and estimates
- Slot reservations
- Controlling costs
- Cloud monitoring
- BigQuery admin panel
- Cloud audit logs
- INFORMATION_SCHEMA
- Query path and common errors
- Secure resources with IAM
- Authorized views
- Secure data with classification
- Encryption
- Data discovery and governance
- Scheduling queries
- Scripting
- Stored procedures
- Integration with Big Data products
- 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