BigQuery Fundamentals for Teradata Professionals

This introductory module summarizes the key details of BigQuery architecture and resource provisioning including how BigQuery utilizes slots to execute SQL queries and workload management in BigQuery. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences between Teradata and BigQuery architecture and resource provisioning to help you get started with BigQuery.

This module summarizes the key details of BigQuery's resource hierarchy and data definition model, including how to create datasets and tables in BigQuery. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences between the Teradata and BigQuery resource hierarchies and primary data types to help you start working with data in BigQuery.

This module summarizes the key details of the Google Cloud Identity and Access Management (IAM) model, including how roles and permissions are applied to datasets and tables in BigQuery. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences in roles and permissions between Teradata and BigQuery to help you start securing and sharing your data in BigQuery.

This module summarizes the primary options and best practices for ingesting data into BigQuery, including batch data loading, streaming ingestion, and queries to external data sources. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences in data ingestion options between Teradata and BigQuery to help you start reading and loading your data into BigQuery.

This module summarizes common patterns and best practices for designing and optimizing table schemas in BigQuery, including the use of nested and repeated fields, partitioning, and clustering. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences in schema usage and design between Teradata and BigQuery to help you start structuring and optimizing your data in BigQuery.

This module summarizes the key features and operations of the Google Standard SQL dialect used in BigQuery and best practices for optimizing query performance and controlling costs in BigQuery. Drawing upon your knowledge of Teradata, this module also provides a high-level overview of the similarities and differences in the SQL dialects and features between Teradata and BigQuery to help you start running and optimizing queries in BigQuery.