Streaming music content and big data

In my thesis, I focused on Big Data, Spotify and YouTube Music as well as the connection between all three. I describe the main characteristics of Big Data. Next I focus on how Big Data is processed and stored. Since Big Data is divided into structured and unstructured data, I explain why and in what way we store which, and which storage systems we know.

When we processing data I explain the processing system and describe the processing phases in more details. For a better understanding I drew a pricture of the system, how it works.

In the second part of the thesis I focus on music providers Spotify and YouTube Music.

I describe how the two music providers work, what their priorities are. I write down how similar/different they are. I made a table for an easier comparison, where the data are collected in a condensed form.

I continue a research how Spotify and YouTube Music use Big Data to develop personalized content for users with goal of satisfying them so that they then remain or become long-term customers. I find that Spotify and YouTube Music take data from individuals and use that data to create new playlists that are meant to give people new ideas based on their listening history.

I made a script that enables the export of a user’s playlist from Spotify. This can be transferred to some other provider using this script, for example Apple Music, which is very convinient.

I set up four hypotheses, which in the end I comfirmed, partially confirmed or rejected. In the hypotheses I asked myself whether the two music providers are comparable and determined the number of main characteristics of Big Data, where I came across different solutions, compared them with each other and came to a conclusion.