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.
Ce que vous allez apprendre
- 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
Prérequis
- 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.
Public cible
- Customers
Programme de la Formation
5 modules pour maîtriser les fondamentaux
Objectifs
- Compare architecture and provisioning of resources in Amazon Redshift and BigQuery
- Describe the concept of a slot in BigQuery
Sujets abordés
- →Quick reminder of Amazon Redshift architecture
- →Overview of BigQuery architecture
- →Separation of compute and storage in BigQuery
- →BigQuery Slots
- →Workload management in 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.
Prochaines sessions
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- Tarifs dégressifs (plusieurs places)
- Session privée ou sur-mesure
- En présentiel ou à distance