Understanding Cloud Spanner
In this course, you learn about Cloud Spanner. You will get an introduction to Cloud Spanner and understand how it differs from other database products. You also learn when and how to use Cloud Spanner to solve your relational database needs at scale.
What you will learn
- Build scalable, managed, relational databases by using Cloud Spanner.
- Create and manage Cloud Spanner databases by using the CLI, Terraform, Python API, and the Google Cloud console.
- Program and run queries and transactions by using the Cloud Spanner API.
- Integrate Cloud Spanner with applications.
Prerequisites
- Some prior Google Cloud experience at the fundamental level and experience with relational databases, the SQL language, and some programming are assumed.
Target audience
- Customers
Training Program
7 modules to master the fundamentals
Objectives
- Explain the core concepts and features of Cloud Spanner.
- Understand how Cloud Spanner fits in the CAP theorem.
- Describe the history of Cloud Spanner.
- Explain Cloud Spanner use cases.
Topics covered
- →What is Spanner?
- →Spanner and the CAP Theorem
- →History of Spanner
- →Cloud Spanner Use Cases
Objectives
- Architect Cloud Spanner instances based on location, capacity, availability, and cost.
- Create Spanner instances by using the Google Cloud console, Google Cloud CLI, and Terraform.
- Create Spanner databases by using SQL.
Topics covered
- →Planning Spanner Instances
- →Automating Instance Creating
- →Creating Databases in Spanner
Activities
Lab: Creating Spanner Instances and Databases (Console)
Lab: Creating Spanner Instances and Databases (CLI and Terraform)
Objectives
- Optimize schemas for Spanner architecture.
- Choose appropriate primary keys.
- Manage relationships with primary and foreign keys and with interleaved tables.
Topics covered
- →Spanner Architecture
- →Choosing Primary Keys
- →Defining Database Schemas in Spanner
- →Understanding Interleaving and Foreign Keys
- →Understanding Secondary Indexes
Activities
Lab: Choosing Primary Keys
Lab: Managing relationships with Foreign Keys and Interleaved Tables
Objectives
- Authenticate users and applications that access Spanner databases using Identity Access Management.
- Program Spanner applications using Google Cloud client libraries and Python.
- Optimize queries using strong reads, stale reads, and indexes.
- Manage transactions in Spanner.
Topics covered
- →Authentication and Authorization
- →Using the Spanner Client Libraries
- →Running Queries
- →Managing Transactions
Activities
Lab: Programming Spanner Applications with Python
Lab: Running Queries and Transactions
Objectives
- Deploy Spanner applications to Google Cloud serverless runtimes.
- Migrate data to and from Cloud Spanner by using Dataflow jobs and Apache Beam.
Topics covered
- →Using Spanner from Applications
- →Building Data Pipelines into and out of Spanner
Activities
Lab: Deploying Spanner Applications with Cloud Functions and Cloud Run
Lab: Migrating Data to and from Spanner with Dataflow
Objectives
- Administer Cloud Spanner instances.
- Backup, restore, import, and export data.
- Modify database schemas with no downtime.
- Monitor your Cloud Spanner databases and applications.
Topics covered
- →Managing your Data in Spanner
- →Managing Change Operations
Activities
Lab: Reconciling Account Data with Cloud Spanner Change Streams
Lab: Leverage the Autoscaler Tool for Cloud Spanner to Achieve Workload Elasticity
Objectives
- Review best practices for using Cloud Spanner.
Topics covered
- →Spanner Best Practices
- →Challenge Lab
Activities
Challenge Lab: Administering a Spanner Database
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.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote