Su base de datos de SQL en la nube

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Google Cloud Platform Big Data and Machine Learning Fundamentals en Español

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Google Cloud Platform Big Data and Machine Learning Fundamentals en Español

124 个评分

Este curso acelerado on demand de 1 semana de duración presenta a los participantes las funciones de macrodatos y aprendizaje automático de Google Cloud Platform (GCP). Ofrece una descripción general breve de Google Cloud Platform y profundiza en las funciones de procesamiento de datos. Al final del curso, los participantes podrán realizar lo siguiente: • Identificar el propósito y el valor de los productos clave de Google Cloud Platform relacionados con los macrodatos y el aprendizaje automático • Usar CloudSQL y Cloud Dataproc para migrar cargas de trabajo existentes de MySQL y Hadoop/Pig/Spark/Hive a Google Cloud Platform • Utilizar BigQuery y Cloud Datalab para realizar análisis interactivos de datos • Elegir entre Cloud SQL, BigTable y Datastore • Entrenar y usar una red neuronal mediante TensorFlow • Elegir entre diferentes productos de procesamiento de datos en Google Cloud Platform Para inscribirse en este curso, los participantes deben tener aproximadamente un (1) año de experiencia en uno o más de los siguientes: • Un lenguaje de consultas común, como SQL • Actividades de extracción, transformación y carga • Modelado de datos • Aprendizaje automático o estadísticas • Programación en Python Notas sobre la cuenta de Google: • los servicios de Google no están disponibles en China en la actualidad. Buscando la versión en español de este curso? Visita https://www.coursera.org/learn/gcp-big-data-ml-fundamentals-es/ このコースの日本語版をお探しですか？https://www.coursera.org/learn/gcp-big-data-ml-fundamentals-jp/

从本节课中

Módulo 3: Análisis de Datos en la Nube

En este módulo, presentaremos los casos de uso más comunes de Big Data que Google administrará por usted. Se trata de las actividades que la industria lleva a cabo más a menudo en la actualidad y para las que ofrecemos una migración sencilla a la nube.

Su base de datos de SQL en la nube5:15

与讲师见面

Google Cloud Training

So let's start off by looking at how to do a relational database in the cloud.

So as we mentioned, the data that we have is relatively small,

a few hundred gigabytes maybe, maximum.

So that's the kind of thing that cloud SQL handles perfectly well.

So we would use cloud SQL,

the capacity of the gigabytes is fine a relational offering.

You use it like a relational database, you do SELECTs, you do INSERTs,

you do updates, use deletes.

And you can update an individual field, so that's what cloud SQL is.

Later on, we look at other access patterns that we could

use if we needed to go a larger scale,

or we want to basically deal with objects rather than relational data.

Or if we need to deal with high throughput, or we if we needed to

deal with petabytes of data and still get very quick, timely responses.

But right now, based on our access background of relatively

small data that we want to use in a very familiar way, cloud SQL is a great choice.

So cloud SQL is essentially MySQL, MySQL is an open source database,

and cloud SQL is Google-managed MySQL.

At this point, you gotta be wondering, wait a minute,

in the last chapter, we looked at Cloud Launcher.

And if I wanted to run WordPress on Google, I could just go to Cloud Launcher

and get a computing VM that's pre-installed with WordPress ready to go.

Why can't I do that with MySQL, why does Google have to manage MySQL?

Isn't it just getting a computing VM and installing MySQL on it?

Well, not quite, by having Google Cloud

manage MySQL, we get a few extra benefits,

first benefit, flexible pricing.

One of the things that you could do is to say, well,

I have this database and it's going to be used only between 9 and

5, so between 5 and 9, just passivate the database.

And so you don't have to pay for it when it's passivated, so that's great.

And that's something that you can just have cloud SQL do for you,

you don't have to write any extra infrastructure handling to passivate

the database when you're not using it.

So this is particularly useful for things like test databases,

a database that needs to be run only when a unit test is running.

If a unit test isn't running, we don't need the database,

we don't have to use it.

Second advantage, it's familiar, it's MySQL, and so

every workload that you have that uses MySQL,

it's a prime candidate to run on cloud SQL.

But we're talking about the advantages that running it on

Google can give your relational data as workloads.

So first of all is a flexible pricing, you don't have to pay for

a machine if you aren't using it.

And this is particularly useful for machines that aren't used 24/7.

Secondly, Google manages the backups.

So you can just go to cloud SQL and say, I would like you to back up this database

every Thursday at 2 AM, and it will happen.

Thirdly, you can connect to it from everywhere.

It's on the cloud, so you don't have to worry about your company firewall and

things like that.

You have a database that needs to be connectable from anywhere,

this is a good solution for it.

You get automatic replication if you need to,

you also get very fast connections from GCE,

Google Compute Engine, and GAE, Google App Engine.

Why is that, well, because cloud SQL is on a machine that lives

in Google Cloud, and all these things are also in Google Cloud.

So as long as you're running these apps in the same region,

they get to take advantage of Google's petabit

per second interconnect networking, and

that bisection of bandwidth of one petabit a second.

So that's extremely, extremely fast, and so you get really fast connections

within MySQL and other applications that are running on Google Cloud.

And finally, not the least, is this idea that you

have Google security taking care of your instances.

So you're not at the mercy of a perennially

rotating set of security people.

You basically have it being secured by people who do it as their job day in and

day out.

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