Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
In this course, you learn about the internals of BigQuery and best practices for designing, optimizing, and administering your data warehouse. Through a combination of lectures, demos, and labs, you learn about BigQuery architecture and how to design optimal storage and schemas for data ingestion and changes. Next, you learn techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools. You also learn about the different pricing models. Finally, you learn various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.
Ce que vous allez apprendre
- Describe BigQuery architecture fundamentals.
- Implement storage and schema design patterns to improve performance.
- Use DML and schedule data transfers to ingest data.
- Apply best practices to improve read efficiency and optimize query performance.
- Manage capacity and automate workloads.
- Understand patterns versus anti-patterns to optimize queries and improve read performance.
- Use logging and monitoring tools to understand and optimize usage patterns.
- Apply security best practices to govern data and resources.
- Build and deploy several categories of machine learning models with BigQuery ML.
Prérequis
- Introduction to Data Engineering
Public cible
- Data analysts, data scientists, data engineers, and developers who perform work on a scale that requires advanced BigQuery internals knowledge to optimize performance.
Programme de la Formation
11 modules pour maîtriser les fondamentaux
Objectifs
- Explain the benefits of columnar storage.
- Understand how BigQuery processes data.
- Explore the basics of BigQuery's shuffling service to improve query efficiency.
Sujets abordés
- →Introduction
- →BigQuery Core Infrastructure
- →BigQuery Storage
- →BigQuery Query Processing
- →BigQuery Data Shuffling
Activités
Labs and demos
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
Former plusieurs collaborateurs
- Tarifs dégressifs (plusieurs places)
- Session privée ou sur-mesure
- En présentiel ou à distance