Compute on Google Cloud Platform

1

Introducci贸n al curso de Google Cloud for Developer Community

2

Lectura: introducci贸n de instalaci贸n

3

Tutorial de Qwiklabs

4

C贸mputo en la nube de Google

5

Opciones de c贸mputo en la nube

6

M谩quinas virtuales a profundidad

7

Tutorial para instalar Qwiklabs

8

Demo: m谩quinas virtuales a profundidad

9

C贸mputo sin administraci贸n con plataformas como servicio

10

Demo: c贸mputo sin administraci贸n

11

Lectura: 驴qu茅 son los contenedores?

12

C贸mputo contenerizado con App Engine Flex

13

C贸mputo contenerizado con Cloud Run

14

Funciones serverless

Continuous Integration, Continuous Delivery

15

CI/CD en Google Cloud Platform

16

Estrategias de Despliegue

17

Repositorios de c贸digo

18

Construcci贸n y despliegue de artefactos

19

Infraestructura como c贸digo

20

Despliegue en Servicios Serverless

Google Kubernetes Engine

21

Kubernetes Overview

22

Demo Kubernetes

23

Planeaci贸n de tu despliegue

24

Anthos

25

Cloud Run for Anthos

26

Demo Cloud Run for Anthos

27

Anthos Service Mesh

28

Site Reliability Engineering con Anthos

Streaming Data Analytics

29

Integraci贸n de datos e ingesta de datos totalmente administrada sobre GCP

30

Demo: ingesta de datos

31

Ingesta de datos confiable en streaming sobre GCP

32

Demo: ingesta de datos confiable

33

Demo: configuraci贸n de Apache Kafka

34

Visualizaci贸n de mensajes de una base de datos relacional en Google Cloud

35

Data Warehouse: el modelo tradicional para construir un repositorio de datos empresarial

36

Data Lakehouse: el nuevo y moderno enfoque para construir un repositorio de datos empresarial

37

El portafolio de gesti贸n de datos en Google Cloud

38

Desglose del portafolio de gesti贸n de datos (Bases de datos) en Google Cloud

39

Gobierno de datos de punta a punta para garantizar la seguridad en tu Data Lake

40

Gobierno de datos: calidad y monitoreo

Machine Learning

41

驴Qu茅 es ML y AI?

42

Plataforma de AI en GCP

43

Auto ML con datos estructurados

44

Demo Auto ML con datos estructurados

45

Predicci贸n de tarifas usando AI notebooks

46

Demo predicci贸n de tarifas usando AI notebooks

47

TensorFlow Extended

Sesiones en vivo

48

Sesi贸n en vivo con Pablo P茅rez Villanueva

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Resources

What does the Google Cloud portfolio for data management look like?

In the dynamic world of data management, Google Cloud offers a diverse portfolio of solutions to meet the most diverse business needs. With options ranging from in-memory databases to robust analytics platforms, Google Cloud is positioned as an invaluable ally in data management. Understanding the features and applications of each type of database is key to getting the most out of these technologies.

What does Google Cloud offer in-memory databases?

In-memory databases are critical for applications that require real-time responses. In the context of Google Cloud, these databases support the Redis and Memcached protocols. They are ideal for use cases such as eCommerce, where speed is essential and changes in data, such as prices, are rare, ensuring sub-second queries.

What are the noSQL database options?

Google Cloud offers two outstanding solutions within its noSQL portfolio:

  • Cloud Firestore: This is a document-type database, ideal for content management applications. It allows the use of a query language similar to SQL to efficiently manage collections and documents.
  • Bigtable: Specialized in storing key-value pairs or columns, BigTable is perfect for time series analysis and IoT data ingestion, providing high read and write speed.

What characterizes relational databases in Google Cloud?

Within relational, Google Cloud presents two main options:

  • Cloud SQL: Compatible with MySQL, PostgreSQL and SQL Server, oriented to online transactional processes (OLTP) and ensuring consistency and reliability of transactions in real time.
  • Cloud Spanner: A powerful solution combining relational and non-SQL database capabilities, capable of handling up to ten thousand queries per second and used by Google services such as Google Ads.

How does Google Cloud approach data analytics?

Google Cloud makes available BigQuery, a serverless analytics solution that scales effortlessly to petabyte and exabyte levels. It offers advanced features such as integrated machine learning, embedded analytics and capabilities for geographic data analysis, positioning itself as an essential tool for advanced analytics projects.

How to choose the right database in Google Cloud?

Determining the right database to deploy or migrate to the cloud may seem challenging. However, Google Cloud offers a structured approach based on several scenarios. Identifying the main focus of the migration or modernization, and selecting the right scenario, is crucial to optimize data management in the cloud.

What are the possible scenarios for migrating or modernizing databases?

Migration scenarios in Google Cloud include:

  1. Rehost: Transfer virtual machines directly to the cloud, gaining scalability.
  2. Lift and Shift: Migrate homogeneous databases, such as MySQL, from on-premises environments to cloud services.
  3. Revise: Migrate heterogeneous databases, such as Oracle to Cloud Spanner, leveraging horizontal scalability.
  4. Rebuild: Develop cloud-native applications using modern technologies, such as noSQL databases.
  5. Replace: Use software as a service, such as BigQuery, which simplifies data management and storage by being serverless.

What tools does Google Cloud offer to facilitate migration?

Google Cloud offers several tools to support the migration process:

  • Migvisor: Tool to assess the complexity of migrations and provide detailed effort and feasibility reports.
  • Streen: Solution to migrate data continuously and without downtime, allowing to keep critical operations active during the migration process.

What is an example of a successful migration with Google Cloud?

Optiva, a telecommunications company, migrated its Oracle database to the Cloud Spanner managed by Google. Thanks to this transition, they not only improved their performance by up to ten times, but also managed to scale efficiently. This case highlights how Google Cloud solutions can transform complex data infrastructures into more efficient and adaptable tools. This is just the beginning; Google Cloud continues to expand its data governance offerings in future classes.

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Tipos de Migraciones en GCP:

  • Rehost (lift and shift to IaaS): Reubicar la misma base de datos pero una m谩quina virtual en la nube, escalabilidad de la nube p煤blica.

  • Refactor (lift and shift to PaaS): Base de datos como servicio, hablado Base de Datos homog茅nea, base de datos del mismo tipo.
    -Revise (modernize to PaaS): Base de datos heterogeneas (diferente motor de base de datos), ejemplo: En sitio tienen una BD Oracle a quiere migrar tu datos hacia Cloud Spanner, escala de forma horizontal.
    -Rebuild (Cloud Native): Creas una aplicaci贸n desde cero (Cloud Native), uso de BD NoSQL.
    -Replace(with SaaS): Software como servicio, no se preocupa de configurar equipos.

Una gu铆a de como encontrar la Base de datos adecuada para tus necesidades de datos.

El evaluar correctamente las caracteristicas de una migraci贸n asi, puede afectar positiva o negativamente a la empresa