Migrating Amazon Redshift Users to BigQuery
In this course you will learn how to translate various concepts in Amazon Redshift to the analogous concepts in BigQuery. You will learn how the high-level architectures of Amazon Redshift and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Amazon Redshift to data types in BigQuery, understand schema mapping from Amazon Redshift to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Amazon Redshift and BigQuery.
What you will learn
- Compare architecture and provisioning of resources in Amazon Redshift and BigQuery
- Configure datasets and tables in BigQuery
- Map and compare data types in Amazon Redshift to data types in BigQuery
- Map and optimize schemas from Amazon Redshift to BigQuery
- Translate SQL from Amazon Redshift to BigQuery
Prerequisites
- Experience using Amazon Redshift as a data warehouse for managing data and performing SQL analysis. Basic experience with BigQuery is recommended, but not required for this course.
Target audience
- Customers
Training Program
5 modules to master the fundamentals
Objectives
- Compare architecture and provisioning of resources in Amazon Redshift and BigQuery
- Describe the concept of a slot in BigQuery
Topics covered
- →Quick reminder of Amazon Redshift architecture
- →Overview of BigQuery architecture
- →Separation of compute and storage in BigQuery
- →BigQuery Slots
- →Workload management in BigQuery
Objectives
- Understand the resource hierarchy in BigQuery
- Configure datasets and tables in BigQuery
Topics covered
- →Resource Hierarchy in Amazon Redshift
- →Resource Hierarchy in BigQuery
- →Creating resources in BigQuery
- →Sharing resources in BigQuery
Activities
Lab: Provisioning and Managing Resources in BigQuery
Objectives
- How data types map from Amazon Redshift to BigQuery
- Understand data types unique to BigQuery
Topics covered
- →Mapping for data types from Amazon Redshift to BigQuery
- →Data types unique to BigQuery
Objectives
- Define schemas in BigQuery
- Implement partitioning and clustering in BigQuery
Topics covered
- →Schema definitions in BigQuery
- →Partitioning in BigQuery
- →Clustering in BigQuery
Activities
Lab: Schema Migration to BigQuery
Objectives
- Understand query capabilities in BigQuery SQL
- Write user-defined functions and procedures in BigQuery SQL
Topics covered
- →SELECT statements
- →DML statements
- →DDL statements
- →UDFs and Procedures
Activities
Lab: Writing SQL for BigQuery
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
- 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).
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
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote