BigQuery Fundamentals for Teradata Professionals
Master BigQuery essentials: architecture, data management, and optimization. Ideal for Teradata experts transitioning to Google Cloud.
Master BigQuery essentials: architecture, data management, and optimization. Ideal for Teradata experts transitioning to Google Cloud.
This course is designed for Teradata professionals looking to expand their skills to BigQuery on Google Cloud. It covers essential BigQuery concepts including architecture, resource provisioning, data definition, security, data ingestion, schema optimization, and SQL querying. The curriculum emphasizes hands-on learning through interactive labs, enabling participants to apply their Teradata knowledge to BigQuery contexts. Key topics include BigQuery's slot-based execution model, IAM roles and permissions, data loading techniques, nested and repeated fields, and query optimization. Throughout the course, comparisons between Teradata and BigQuery are highlighted, facilitating a smooth transition for experienced data warehouse professionals.
Instructors:
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What you'll learn
Describe BigQuery's architecture and resource provisioning model
Create, secure, and share BigQuery data assets using best practices
Implement common patterns for designing schemas in BigQuery
Optimize data ingestion processes for BigQuery
Write and optimize SQL queries in BigQuery's dialect
Compare and contrast Teradata and BigQuery functionalities
Skills you'll gain
This course includes:
4 Minutes PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This course provides a comprehensive introduction to BigQuery for professionals with Teradata experience. It covers BigQuery's architecture, resource provisioning, data definition model, security, data ingestion, schema design, and SQL capabilities. Each module includes comparisons between Teradata and BigQuery, helping learners leverage their existing knowledge while adapting to Google Cloud's data warehousing solution. The curriculum is structured to provide both theoretical understanding and practical skills through hands-on labs, covering topics such as monitoring workloads, securing datasets, optimizing table schemas, and writing efficient SQL queries in BigQuery. By the end of the course, participants will be equipped to design, implement, and optimize data warehousing solutions in BigQuery, drawing on their Teradata expertise to accelerate their transition to cloud-based data analytics.
BigQuery Architecture and Resource Provisioning
Module 1 · 1 Hours to complete
BigQuery Data Definition Model
Module 2 · 16 Minutes to complete
BigQuery and Google Cloud IAM
Module 3 · 1 Hours to complete
BigQuery Data Ingestion
Module 4 · 16 Minutes to complete
BigQuery Schema Design and Optimization
Module 5 · 1 Hours to complete
SQL in BigQuery
Module 6 · 1 Hours to complete
Fee Structure
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Instructor
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