All language subtitles for 010 Extracting Data from the Web_en

Narrator: All right, I've got another quick demo for you.

And this time we're going to be using the Web connector

to scrape data from a Wikipedia page

and then import it into Power Query as a table.

Now, the really cool thing about this Web connector

is that it allows you to connect to web-hosted files,

or we'll scan a webpage, identify any structured tables,

and then provide a list of the tables found

and show you a preview

before loading it into the Query editor.

Now, it's super simple to use and really powerful.

Let's check it out.

All right, so for this demo,

we're going to be using the Wikipedia URL

that was added at the bottom left of that slide.

So feel free to pause the video, rewind a bit,

and then copy the URL so you can follow along.

All right, so what we're going to do here

is head up to a new source,

and Web is actually one of the common connectors here.

So we can just click Web.

So from here, it's as simple

as copying and pasting that URL.

If we were to click Advanced Options,

there's some other pieces here

where we can add different URL parts,

again, kind of command timeouts,

different HTTP requests, and parameters and stuff.

So we're not going to mess around with any of that.

We'll stick to Basic. We'll click OK.

All right, so what Power BI is doing here

is it's going through and connecting to that webpage,

and then it's going to load this very familiar

data preview window, right?

Kind of similar to the MySQL example that we saw.

We have all of the kind of different elements

or tables on the left hand side.

All right, and if we click one of these,

we're going to get a Preview window here.

The other view that you can see here,

that's actually really cool,

is you can actually click on this Web View,

and it actually shows you what the webpage looks like.

All right, so you can scroll through and, you know,

let's say we want to import this table

with rank, firm, company, country.

All right, we can see that this largest company's table

that we've selected, rank, firm, company, country,

is that table.

All right, if we started clicking

through some of these other tables, you know,

we're basically exploring the different table elements

that have been discovered within that page.

So if any of these other ones make sense to connect to,

you certainly could.

For the sake of this example,

we're going to connect to this table.

All right, so now that the data is in the Query editor,

we can go through and follow the same process

that we've been using for our other tables.

All right, we'll go through and we can check

that our data types and our column headers are appropriate.

All right, so we've got a whole number here.

Text makes sense for firm and company, and also for country.

And then for this, AUM column,

right, assets under management,

this is actually in billions of dollars.

So we could update this to currency

or leave this as a whole number here.

And there are some other transformation steps

that you could apply here to show the actual value here

and not have it shortened.

So again, everything looks good there.

You know, we could update this name

to Largest Asset Management Firms,

and then we'd be good to go.

All right, so that is how you can use the Web connector

to scrape data from a website

and import any sort of tables that are visible

within that page.

The last step that we'll do here

is similar to our Fuzzy Factory data.

We're going to disable the load.

And finally, we'll save our work.

All right, up next we're going to dig into

some of the data QA and profiling tools

within the Query editor.