In this course I'll be providing a very quick overview of GCP,

in particular its foundational parts to compute and storage.

And then we dive straight in to provide a deeper overview of

the different ways you can process data with GCP.

So we'll talk about BitQuery, Dataflow, Dataproc, Cloud SQL, Datalab, etc.

So who's this class meant for?

It's for meant for what we call data engineers.

People who design, build, maintain data structures,

databases, data processing systems, data pipelines.

Who do extraction, transformation, loading of data,

move data from one place to another.

Who may be data scientists who are analyzing data,

enabling machine learning to happen, maybe even doing machine learning yourself.

People who model business processes,

who enable data driven decision making within your company.

So if you recognize yourself in any of these things this class is meant for you.

It's meant for anyone who'll be working with data on Google Cloud Platform.

It's also meant for any decision makers who are trying to decide

whether your company should move to GCP and do your data processing on GCP.

So this gives you a good overview of all the capabilities that the Google Cloud

provides so that you can make an informed decision.

In the course we will now start with an overview of

Google Cloud Platform as a whole, but with particular

emphasis on the ways you can handle data in the platform.

The way you can ingest data, different ways of doing processing of data,

whether with map produce, such as with Spark or

whether with a streaming mechanism like with Dataflow.

We look at BigQuery, which is our auto scaling date warehouse, etc.

So we'll give you overview of GCP, but

with an emphasis on the data handling parts of it.

We'll also then move on to talking about the foundation of GCP and

this is computing and storage.

Like any computer, and the Cloud is a computer, the two key parts

of a computer are the computing units and the storage units of persistent data.

And the way that happens on the Cloud is with Compute Engine and Cloud Storage, so

we'll talk about both of those.

And then we'll move on to things that you're probably doing today,

that you could easily move to the Cloud.

We'll talk about the use cases that are quite common that Google provides a good

managed environment for such that you can take things you're doing on premise,

on your own hardware, and move it to the Cloud quite easily.

Because the same software that you may be using Is also present on the Cloud.

So as examples of those use cases, we will talk about relational databases

in the form of Cloud SQL, which is a MySQL database hosted on GCP.

And we'll talk about Cloud Dataproc, which is a hosted

version of big Spark hype of the Hadoop ecosystem software processes.

So in order to do that we'll look at how to import data and

how to query MySQL running in Google Cloud.

And similarly, we'll also look at how to take a Spark program, submit it,

and have it run on Cloud Dataproc.

And the Spark program that we will look at will be a machine-learning program to

carry out recommendations.

And once we have talked about the use cases that we're

talking about, migrating use cases, we will then move

on talking about more transformational use cases.

These may be things that you may not be doing today,

mainly because it may not be possible for you to do them today.

So for example, we look at how you could query

petabytes of data in a matter of seconds with Google BigQuery.

We will talk about how to do fast random access,

trading off global consistency versus low global availability.

We'll also talk about machine learning.

We'll talk about how to do TensorFlow.

Maybe you run TensorFlow,

but very commonly maybe running it on a single machine.

We'll talk about how you'd run TensorFlow in a distributed fashion

on the Cloud over extremely large data sets.

And then we will move onto just providing a very quick

overview of how you would do scalable reliable data processing on

Google Cloud with Cloud Pub/Sub, which is a messaging architecture and

with Cloud Dataflow, which is a way to execute a code

that processes both streaming data and batch data in essentially the same way.

And finally, we'll come to the conclusion of the course, and

I'll leave you with some resources for further reading.

Resumen de Curso y Agenda

Google Cloud Platform Big Data and Machine Learning Fundamentals en Español

Conoce a los instructores