In this lecture, we will present the seven variables which we want to extract from the MrBeast YouTube
channel for each of his videos.
Here we have an example of the seven video parameters we are interested in for a particular video.
Going through them briefly, video ID is a unique identifier for each video title is.
The title of the video published at is when the video was published.
Duration is how long the video is and views, likes and comment counts.
We have already seen in the previous lecture.
Now, in order to get these variables when using the YouTube API, we have to jump to competitors.
The first hurdle being getting the ID of the playlist that contains all the channels uploaded videos.
The second hurdle is using the playlist ID we just found.
We then get the list of unique video IDs, and once we have these unique IDs, we can then finally get
all the variables we are after.
To help us conceptualize this better, we can use the Russian nesting doll analogy.
If you're not familiar with these dolls, they are a set of usually wooden dolls of decreasing size
placed one inside another.
So if we were to use this analogy and what we learn from getting familiar with the YouTube API.
Lecture.
When we mentioned resources, we can summarize this lecture as follows.
The biggest doll represents the first hurdle where we will use the channel's resource to get the playlist
ID.
Once we have the playlist ID, we can open the biggest doll where there we will find the next biggest
one.
This represents the list of unique video IDs which we will get from the resource playlist items.
When finally we get the list of video IDs, we have the smallest Russian doll which relates to the videos
resource where.
Here we will get the final seven video variables.
That's all the theory that we will need to cover in the coming four lectures.
We will translate what we learned in the previous lectures to Python code.
So if we were to summarize, the first three out of the four remaining lectures represent the three
Russian dolls we introduce in this lecture.
So part one will focus on getting the playlist ID, part two the video IDs, and part three the video
data.
And in the fourth and final lecture, we will save the data we extracted to JSON, ready to be ingested
into the data warehouse.
That's all for now and I can't wait to start coding this up with you.