WEBVTT

00:01.090 --> 00:07.720
So I'm now going to show you an example in the code how our data structure is going to be for the scraped

00:07.720 --> 00:09.020
data that we're getting.

00:09.040 --> 00:11.850
You don't have to type this out along with me.

00:11.860 --> 00:16.580
It's just to show you an example of exactly how our data is going to look like.

00:16.600 --> 00:22.720
So we have here I can make an example here of an array of objects that we have.

00:22.990 --> 00:26.980
So we have const scraping results.

00:29.080 --> 00:29.950
This is an array.

00:29.950 --> 00:36.220
And then we have one example object here, and this example object has different properties.

00:36.460 --> 00:43.510
So we have different properties for each data we're scraping, such as the title date, it was posted,

00:43.540 --> 00:45.430
the neighborhood and so on.

00:45.430 --> 00:50.290
So we can have the title here as the job title.

00:51.170 --> 00:54.260
So we have entry level software engineer.

00:55.360 --> 00:57.370
Right here and it will look like this.

00:57.400 --> 00:59.890
It's a property with a string here.

01:00.190 --> 01:03.100
And then we're going to have the date it was posted.

01:03.100 --> 01:04.540
So date posted.

01:04.660 --> 01:05.800
Now the date.

01:05.800 --> 01:07.810
We are the date.

01:08.700 --> 01:17.370
Data is going to be a JavaScript date object because we can actually get a more precise date from the

01:17.940 --> 01:19.790
HTML of this page.

01:19.800 --> 01:22.350
I'm going to show you that in a later section.

01:22.440 --> 01:26.490
But for now, just know that we're saving it as a date object.

01:26.490 --> 01:35.310
So it will be a date object, something like a date we have here A with the month and a date and then

01:35.580 --> 01:40.620
also a time it was posted and.

01:41.770 --> 01:46.720
Then we have the neighborhood the posting was on.

01:46.750 --> 01:49.120
So neighborhood.

01:50.690 --> 01:54.830
Here we have Palo Palo Alto.

01:55.900 --> 01:56.740
And.

01:57.600 --> 02:03.750
Then we have the job description URL, which is the URL we get when we click on here.

02:04.560 --> 02:06.480
So it's going to be this one.

02:06.480 --> 02:08.580
I'm just going to copy and paste that.

02:08.580 --> 02:09.960
So your URL.

02:12.600 --> 02:15.270
And then we have the job description.

02:19.770 --> 02:22.560
And that's basically all of the text we have here.

02:22.560 --> 02:24.720
I'm not going to copy and paste that in.

02:24.720 --> 02:30.030
I'm just taking a little snippet here just as an example here.

02:32.020 --> 02:34.900
And then we also have a compensation.

02:35.170 --> 02:36.610
A compensation?

02:37.090 --> 02:40.030
So compensation.

02:43.520 --> 02:45.800
And that's going to be this one.

02:48.930 --> 02:51.030
And I'm missing a comma here.

02:51.030 --> 02:53.040
So there we go.

02:53.040 --> 02:57.060
So that is how our data is going to be looking like.

02:57.060 --> 03:04.830
We are saving this data in MongoDB database in the end where we have an array of these different scraping

03:04.830 --> 03:09.780
results and each scraping result have these different properties.

03:10.440 --> 03:15.960
So now in the next section I'm going to show you how we start out scraping the data from the actual

03:15.960 --> 03:16.410
site.

03:16.410 --> 03:22.410
We are going to start out by getting the titles of each of these jobs, so I'll see you in the next

03:22.410 --> 03:23.250
section.
