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Move storage off-cluster with Google Cloud Storage

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Google Cloud Platform Big Data and Machine Learning Fundamentals

4.6 (6,637 ratings) | 70K Students Enrolled

Course 1 of 5 in the Data Engineering, Big Data, and Machine Learning on GCP Specialization

This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • Google services are currently unavailable in China.

Skills You'll Learn

Tensorflow, Bigquery, Google Cloud Platform, Cloud Computing

Reviews

4.6 (6,637 ratings)

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DK

Apr 08, 2019

This course provides the basics of Google Cloud Platform and makes us understand Google Products and services on Cloud. I would love to explore GCP more and will opt for the remaining GCP courses.

CR

Dec 27, 2017

This was a great course to understand at a high level how to design and create my data ecosystem and how to do it sustainably. Hopefully, next courses provide more in-depth the technical features.

From the lesson

Recommending Products using Cloud SQL and Spark

In this module you will have an existing Apache SparkML recommendation model that is running on-premise. You will learn about recommendation models and how you can run them in the cloud with Cloud Dataproc and Cloud SQL.

Approach: Move from on-premise to Google Cloud Platform9:50

Demo: From zero to an Apache Spark job in 10 minutes or less6:20

Challenge: Utilizing and tuning on-premise clusters6:05

Move storage off-cluster with Google Cloud Storage4:54

Taught By

Google Cloud Training

Transcript

As you might remember from the demo earlier,

separating compute and storage enables

cost-effective scale for workloads.

With the notion of turning down clusters,

you may be worried about all the data that's

currently stored on disk HDFS.

You may be using HDFS on-premises.

What happens to that with

our new Google Cloud Platform architecture?

Your data is not stored in the cluster,

your data is now stored

off-cluster in Google Cloud Storage buckets.

Really, won't that make things

slow to reach out across a network,

each time the cluster needs some data?

Recall that earlier we mentioned

Google's data center bandwidth between compute and storage.

We talked about a petabit bisectional bandwidth.

What that means is that,

if you draw a line somewhere in

the network the bisectional bandwidth is the rate of

communication at which servers on one side

of the line can

communicate with servers on the other side.

With enough bisectional bandwidth,

any server can communicate with

any other server at full network speeds.

With petabit bisectional bandwidth,

the communication is so fast that it no longer

makes sense to transfer the files and store them locally.

Instead, it makes sense to

use the data from where it is stored.

So here's the plan. We'll use HDFS,

but we'll use HDFS only on

the cluster for working

storage, storage during processing,

but we will store

all actual input and output data on Google Cloud storage.

Because the input and output data is off the cluster,

the cluster can be created for a single job or

type of workload and can be shut down when not in use.

Changing your code that works on prem to

have it work with the data on cloud storage is easy.

Just replace HDFS in your Spark or Big Cloud with GS.

So you take the HDFS URLs,

HDFS :// and replace it with GS

:// This will make the Spark of

big job read or write to cloud storage, that's it.

There's also an H-Base connector for Cloud Bigtable.

So if you have your data in

Bigtable you can use the HBase connector.

There's a Big Query connector

that you can use to work with data,

if the data is in the analytics warehouse.

So when we say off-cluster storage,

we're talking about cloud storage or we are talking

about BigQuery or we are talking about Bigtable.

Cloud Data-proc and Google cloud storage are

tightly coupled with other GCP products.

So storing your data off cluster,

means that you can process this data not just with Spark from

Data-proc but also from all these other products.

Not to mention, storing it off cluster in

GCS is generally cheaper, since: a,

disks attached to computer instances are

expensive in and of themselves and b,

if your data are off cluster,

you'd get to shutdown the computer nodes

when you're not using them.

So bottom line, store your data in Google Cloud Storage.

Friends don't let friends use HDFS.

So let's recap what we've covered.

You can get a cloud data-proc cluster

up and running in about 90 seconds.

This gives you all of the power of Hadoop

without having to manage clusters.

As you saw, you can lift-and-shift

your existing Hadoop and Spark workloads,

by simply replacing HDFS:// URLs with GS:// URLs.

You can connect Cloud Data-proc

to Google Cloud Storage and

unlock the benefits of both scale and cloud economics.

You get to provision a cluster per job if you want to,

and shut the cluster down when you're done.

Lastly, your clusters are customizable,

which could include autoscaling and preemptible VMs

so that you get cost savings.

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