Name: 連携型ビッグデータプラットフォームの構築方法（Google Cloud Next '17 (How to architect a collaborative big data platform (Google Cloud Next '17))
Uploaded: 2021-01-14T06:57:24.000Z
Duration: 33 min 12 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

I know the day's been running a little bit long,

so I will hopefully not keep you too long.

Welcome to the session between you and happy hour.

Hopefully, we'll get you out of here just in time

team for Americas East, for Google Cloud Platform,

and today, I want to talk to you about building

Effectively, you've got teams of individuals spread

across your company, that have to work together

So, we're going to walk through some of the ways

that we tell a lot of customers, and developers, and partners

First, we're going to meet our hypothetical team

about the tasks that have to do, and the tools that they

the tools they use to the tools available in Google Cloud,

get a consistent set of tools and work together.

This is how we do things, like set up teams

to do self-service and easy data discovery.

I'm probably going to trivialize a handful of details,

but stay with me, it'll make sense as we get through it.

And then, I want to cover what the new workflow's

like once you get all this stuff up and running,

All right, so first, we've got about four individuals.

We've got a data scientist, a data engineer, a business

Ultimately, they all have three things in common.

They use different data sets, those data sets

are all in different formats, and they all

So, let's assume for a second, or let's imagine for a second,

is to build a recommendation engine for a retail site.

So, what are the kinds of things that each person on the team

So, the first thing we'll cover is the data,

So, from a data perspective, Alice, the data scientist,

Things that have no longer log files, right,

like purchase history, product history, product metadata,

All the stuff that it would take to actually craft and model

He's going to have to take all of the raw data

So, he's probably going to need things like,

logs information, product transactions or purchase

got to turn into something useful for other parts

and understanding about how the business is performing.

Not just, again, rows or log files, but again,

that next level up from, sort of, the basics.

And then, finally, we have Jan, the app developer.

She's going to need users and product predictions,

to be able to build new microservices that

expose things like this recommendation engine

So, that can be things like a recommendations API,

access to wish lists, some knowledge around similar users

or similar products, how things complement each other.

But, you can see there's a little bit of overlap here,

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連携型ビッグデータプラットフォームの構築方法（Google Cloud Next '17 (How to architect a collaborative big data platform (Google Cloud Next '17))

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