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The Google Cloud Professional Data Engineer Certification Program is a comprehensive program that provides the skills you need to advance your career and provides training to help you prepare for the industry-recognized Google Cloud Professional Data Engineer Certification exam administered by Google Cloud.
What’s in the Data Engineer learning path?
- GCPDE: Data Engineering on Google Cloud
- GCP-DWBQ: Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
- GCP-DICDF: Data Integration with Cloud Data Fusion
- GCP-MTDBQ: Migrating Teradata Users to BigQuery
- GCP-MRSBQ: Migrating Amazon Redshift Users to BigQuery
- GCP-MDMDP: Managing a Data Mesh with Dataplex
Advance your career in data engineering and prepare for the Google Cloud Professional Data Engineer Certification exam with this learning path.
Overview
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.
Skills Covered
- 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
Prerequisites
- There are no prerequisites required to attend this course
Target Audience
- Anyone who are interested
Module 1: Understanding BigQuery Architecture
Topics:
- Quick reminder of Snowflake architecture
- Overview of BigQuery architecture
- Separation of compute and storage in BigQuery
- BigQuery Slots
- Workload management in BigQuery
Objectives:
- Compare architecture and provisioning of resources in Snowflake and BigQuery
- Describe the concept of a slot in BigQuery
Module 2: Creating Datasets and Tables in BigQuery
Topics:
- Resource Hierarchy in Snowflake
- Resource Hierarchy in BigQuery
- Creating resources in BigQuery
- Sharing resources in BigQuery
Objectives:
- Understand the resource hierarchy in BigQuery
- Configure datasets and tables in BigQuery
Module 3: Mapping Data Types from Snowflake to BigQuery
Topics:
- Mapping for data types from Snowflake to BigQuery
- Data types unique to BigQuery
Objectives:
- How data types map from Snowflake to BigQuery
- Understand data types unique to BigQuery
Module 4: Schema Optimization and Mapping
Topics:
- Schema definitions in BigQuery
- Partitioning in BigQuery
- Clustering in BigQuery
Objectives:
- Define schemas in BigQuery
- Implement partitioning and clustering in BigQuery
Module 5: SQL Translation from Snowflake to BigQuery
Topics:
- SELECT statements
- DML statements
- DDL statements
- UDFs and Procedures
Objectives:
- Understand query capabilities in BigQuery SQL
- Write user-defined functions and procedures in BigQuery SQL
Exam & Certification
Note:Â There is no exam directly associated with this course. However, Google Cloud offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2025 and beyond. Obtaining a Google Cloud certified credential is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity, key areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our full range of Google Cloud certifications and start building the skills that matter today.
Training & Certification Guide
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