GCP200MIGSNOWFLAKE

Migrating Snowflake Users to BigQuery

In this course you will learn how to translate various concepts in Snowflake to the analogous concepts in BigQuery. You will learn how the high-level architectures of Snowflake and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Snowflake to data types in BigQuery, understand schema mapping from Snowflake to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Snowflake and BigQuery.

Google Cloud
✓ Official training Google CloudLevel Fundamentals⏱️ 1 day (7h)

What you will learn

  • Compare architecture and provisioning of resources in Snowflake and BigQuery
  • Configure datasets and tables in BigQuery
  • Map and compare data types in Snowflake to data types in BigQuery
  • Map and optimize schemas from Snowflake to BigQuery
  • Translate SQL from Snowflake to BigQuery

Target audience

  • Customers

Training Program

5 modules to master the fundamentals

Objectives
  • Compare architecture and provisioning of resources in Snowflake and BigQuery
  • Describe the concept of a slot in BigQuery
Topics covered
  • →Quick reminder of Snowflake 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 Snowflake
  • →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 Snowflake to BigQuery
  • Understand data types unique to BigQuery
Topics covered
  • →Mapping for data types from Snowflake 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

Related Trainings

SFEIR Institute
Best

dbt

Learn to transform your data with dbt, the leading tool in the Modern Data Stack. You'll start by understanding the evolution of data architectures and the difference between ETL and ELT. You'll install dbt, create your first project and connect it to your data sources. Then you'll learn to build structured data models, choose the right materialization options (table, view, incremental) and organize your metadata with tags. You'll discover how to reference your sources and manage dependencies between models. You'll explore advanced features: seeds to initialize reference data, snapshots to track history and manage slowly changing dimensions, Jinja macros and variables to automate your transformations. Finally, you'll implement automated tests to ensure data quality, document your models with lineage, and discover packages from the dbt community. Hands-on training with 60% labs.

2 d
Fundamental
Google Cloud

Introduction to Data Analytics on Google Cloud

This course is an introduction to data analytics on Google Cloud. It is designed for learners who have no prior experience with data analytics or Google Cloud. The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.

1 d
Fundamental

Upcoming sessions

March 2, 2026
Distanciel • Français
Register
May 21, 2026
Distanciel • Français
Register
July 2, 2026
Distanciel • Français
Register
August 27, 2026
Distanciel • Français
Register
November 5, 2026
Distanciel • Français
Register
November 26, 2026
Distanciel • Français
Register

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.

790€ excl. VAT

per learner