Cloud Pub/Sub and Cloud Datalab

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Google Cloud Platform Fundamentals: Core Infrastructure

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Google Cloud

Google Cloud Platform Fundamentals: Core Infrastructure

4161 ratings

Curso 1 de 6 em Specialization Architecting with Google Cloud Platform

<<< ATTENTION >>> Learners interested in Google Cloud Platform can continue to learn in either of our latest GCP infrastructure specializations. Option 1: The Developing Applications with Google Cloud Platform Specialization is available on Coursera. This brand-new Specialization teaches you to build secure, scalable, and intelligent cloud-native applications. Visit https://www.coursera.org/specializations/developing-apps-gcp for more details. Option 2: The Architecting with Google Cloud Platform Specialization is also available. This Specialization has updated, improved content that will help you if you are preparing for the Google Certified Professional - Cloud Architect exam. Head to https://www.coursera.org/specializations/gcp-architecture to explore the updated content. Google Cloud Platform Fundamentals: Core Infrastructure covers the fundamentals of Google Cloud Platform and is the first course in both specializations. This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, and Google Container Engine. Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China). • If you are a Google Cloud Platform customer with European Union (EU) and Russian billing addresses, read the VAT Overview documentation at: https://cloud.google.com/billing/docs/resources/vat-overview • More Google Cloud Platform free trial FAQs are available at: https://cloud.google.com/free-trial/

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Big Data and Machine Learning in the Cloud

GCP's big-data and machine learning offerings are intended to help customers get the most out of data. These tools are intended to be simple and practical to embed in your applications. This module describes the available big-data and machine learning services and explains the usefulness of each.

Google Cloud Big Data Platform3:20

Cloud Dataflow2:34

Cloud Pub/Sub and Cloud Datalab3:32

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Whenever you're working with events in real time,

it helps to have a messaging service.

That's what Cloud Pub/Sub is.

It's meant to serve as a simple,

reliable, scalable foundation for stream analytics.

You can use it to let independent applications you build send and receive messages.

That way they're decoupled.

So they scale independently.

The Pub in Pub/Sub is short for publishers and Sub is short for subscribers.

Applications can publish messages in Pub/Sub and one or more subscribers receive them.

Receiving messages doesn't have to be synchronous.

That's what makes Pub/Sub great for decoupling systems.

It's designed to provide at least once delivery at low latency.

When we say at least once delivery,

we mean that there is a small chance some messages might be delivered more than once.

So, keep this in mind when you write your application.

Cloud Pub/Sub offers on demand scalability to one million messages per second and beyond.

You just choose the quota you want.

Cloud Pub/Sub builds on the same technology Google uses internally.

It's an important building block for applications where data arrives

at high and unpredictable rates like Internet of Things systems.

If you're analyzing streaming data,

Cloud Dataflow is a natural pairing with Pub/Sub.

Pub/Sub also works well with applications built on GCPs compute platforms.

You can configure your subscribers to receive messages on a push or pull basis.

In other words, subscribers can get notified when new messages

arrive for them or they can check for new messages at intervals.

Scientists have long used

lab notebooks to organize their thoughts and explore their data.

For data science, the lab notebook metaphor works really well,

because it feels natural to intersperse data analysis with comments about their results.

A popular environment for hosting those is Project Jupyter.

It lets you create and maintain web-based notebooks containing

Python code and you can run that code interactively and view the results.

And Cloud Datalab takes the management work out of this natural technique.

It runs in a Compute Engine virtual machine.

To get started, you specify

the virtual machine type you want and what GCP region it should run in.

When it launches, it presents an interactive Python environment that's ready to use.

And it orchestrates multiple GCP services automatically,

so you can focus on exploring your data.

You only pay for the resources you use.

There is no additional charge for Datalab itself.

It's integrated with BigQuery, Compute Engine,

and Cloud Storage, so accessing your data doesn't run into authentication hustles.

When you're up and running,

you can visualize your data with Google charts or

map plot line and because there's a vibrant interactive Python community,

you can learn from published notebooks.

There are many existing packages for statistics,

machine learning, and so on.

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