Curso Understanding Cloud Spanner

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Duración del curso
18 horas
Lugar de impartición
Madrid / Online
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Modalidad
Presencial, Aula Virtual
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Fecha de inicio
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Acerca del curso

In this course you will learn about Cloud Spanner. You will get an introduction to Cloud Spanner, contrasting it with other Database products to understand when and how to use Spanner to solve your relational database needs at scale. You will learn how to create and manage Spanner databases using various tools on Google Cloud, learn to optimize relational schemas with Spanner’s distributed database model in mind, interact with your Spanner databases using the Spanner APIs, integrate Spanner with your applications, and learn how to use other Google tools for administering Spanner databases and managing your data. The lab culminates with a challenge lab where you demonstrate your knowledge of administering Spanner databases and managing data.

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Database administrators, engineers/developers, and cloud architects who want to learn how to create, optimize, and manage Spanner databases and migrate existing databases into Spanner.

  • Build scalable, managed, relational databases using Google Cloud Spanner.
  • Create and manage Spanner databases using the CLI, Terraform, Python API, and the Cloud Console.
  • Optimize relational database schemas for Spanner's distributed database model.
  • Leverage Google Cloud tools for administering Spanner databases and managing data.
  • Program and run queries and transactions using the Spanner API.
  • Integrate Spanner with your applications.

Some prior Google Cloud experience at the fundamental level is assumed. Experience with relational databases, the SQL language, and some programming is also assumed.

Lecture

Module 1: The Need for Spanner

  • What is Spanner?
  • Spanner and the CAP Theorem
  • History of Spanner
  • Cloud Spanner Use Cases

Module 2: Getting Started with Spanner

  • Planning Spanner Instances
  • Automating Instance Creating
  • Creating Databases in Spanner

Module 3: Optimizing Spanner Schemas

  • Spanner Architecture
  • Choosing Primary Keys
  • Defining Database Schemas in Spanner
  • Understanding Interleaving and Foreign Keys
  • Understanding Secondary Indexes

Module 4: Programming Spanner Applications, Queries, and Transactions

  • Authentication and Authorization
  • Using the Spanner Client Libraries
  • Running Queries
  • Managing Transactions

Module 5: Deploying Spanner Applications

  • Using Spanner from Applications
  • Building Data Pipelines into and out of Spanner

Module 6: Spanner Administration

  • Managing your Data in Spanner
  • Managing Change
  • Operations

Module 7: Capstone Project

Lab

Creating Spanner Instances and Databases (Console)

  • Compare Spanner configuration options.
  • Create Spanner databases using the Console.
  • Create Spanner databases using the PostgreSQL dialect.

Creating Spanner Instances and Databases (CLI and Terraform)

  • Create instances and databases using the gcloud CLI.
  • Automate Spanner infrastructure using Terraform.

Choosing Primary Keys

  • Generate Spanner primary keys as UUIDs.
  • Convert counters and timestamps into values appropriate for Spanner primary keys.

Managing Relationships with Foreign Keys and Interleaved Tables

  • Create a relational database with proper primary keys and relationships optimized for Spanner
  • Leverage indexes to improve read performance in Spanner

Programming Spanner Applications with Python

  • Use Python to create and delete Spanner instances and databases.
  • Program Spanner databases that use the PostgreSQL dialect.

Running Queries and Transactions

  • Run parameterized queries using indexes against a Spanner database using the Python Client library.
  • Execute transactions against a Spanner database.

Deploying Spanner Applications with Cloud Functions and Cloud Run

  • Deploy Cloud Functions that read and write to Spanner databases.
  • Set up and use the Spanner emulator for development.
  • Build a REST API that allows you to read and write Spanner data.
  • Deploy a REST API to Google Cloud Run.

Migrating Data to and from Spanner with Dataflow

  • Write ETL pipelines using Apache Beam.
  • Run Apache Beam pipelines using Google Cloud Dataflow.

Leverage the Autoscaler Tool for Cloud Spanner to Achieve Workload Elasticity

  • Configure the Autoscaler and environment
  • Deploy the Autoscaler
  • Observe the autoscaling

Challenge Lab: Administering a Spanner Database

  • Create a Spanner database and import existing data.
  • Backup and restore the database.
  • Export Spanner data and import it into BigQuery.
  • Deploy a data access API that allows access to your Spanner database.
  • Use the Operations monitoring tools for dashboards, uptime checks, and alerts.

Documentación Oficial de Google Cloud - Understanding Cloud Spanner

  • Formador certificado por GCP.
  • Más de 5 años de experiencia profesional.
  • Más de 4 años de experiencia docente.
  • Profesional activo en empresas del sector IT.

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