YouTube dataset: Essential Data for Music Pros

Matic Magister

Dec 9, 2025

How to Build a Custom YouTube Dataset with Viberate

Viberate provides a flexible system that lets you create your own dataset by adjusting several key parameters. Instead of working with rigid or pre‑defined files, you can shape the dataset to match your use case. This makes it useful for professionals who need specific types of information, whether they are analyzing artist growth, segmenting markets, or preparing data for machine‑learning projects.

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You begin by selecting filters that narrow down the dataset. You can choose the country to focus on regional trends or compare how artists perform in different markets. Genre and subgenre options help you isolate scenes, such as drill, afrobeats, hyperpop, techno, or other niches. Selecting the record label filter lets you examine catalog performance or map out competitive landscapes. You can also adjust the data range, which includes metrics like Spotify followers and YouTube views, allowing you to build a file that reflects current interest or long‑term growth patterns.

Once you set your filters, you can export your YouTube dataset as a CSV file through the Viberate interface. CSV files integrate smoothly with analytical tools, making it easy to run comparisons, build dashboards, or share datasets with team members. The platform is designed to help you move directly from data selection to analysis without added complexity.

For quick access, explore the YouTube dataset options available at Viberate here: .

What Music Datasets Can You Obtain on Viberate?

The platform provides several dataset types that cover different parts of the industry. Artist datasets include detailed performance and profile information. You gain access to rankings, artist names, associated countries, genres, and labels. Alongside these attributes, you also receive platform‑specific numbers such as Spotify ranking, YouTube subscribers, and other social metrics. These insights help you understand how well artists perform globally and which signals point to upward movement.

Festival datasets are structured differently. They contain information about names, locations, genres, festival sizes, Viberate rankings, and lineup rankings. These files are helpful for professionals working in booking, programming, or market research. You can analyze festival ecosystems, identify which lineups are gaining attention, or track category‑specific patterns across countries.

Each dataset is built to provide a consistent structure so you can combine or compare them when needed. Whether you are studying micro‑genres, mapping regional performance, or analyzing large‑scale industry behavior, the goal is to give you a clean base of information.

Working with CSV Exports and Machine‑Learning Ready Data

Viberate lets you export your selected dataset directly as a CSV file, which is helpful if you prefer to work offline or need to integrate the information into internal tools. CSV files load smoothly into spreadsheets, BI platforms, and programming environments, so you can merge multiple exports, compare sources, or enrich the data with your own fields. The export process itself is simple: once you set your filters, you download the file and start working with it immediately, without manual data gathering.

For more advanced use cases, Viberate also offers datasets structured for machine‑learning workflows. 

These files include unified, consistent fields that make it easier for models to interpret artist metrics. With a clean structure in place, you can build classification models, forecast engagement patterns, estimate growth curves, or train recommendation systems. Because the data requires minimal preparation, you spend more time experimenting with ideas and less time cleaning or reorganizing fields.

Viberate also supports specialized datasets structured for machine‑learning applications. These datasets include clean, unified fields that help models interpret artist performance metrics. The structured format makes it easier to build classification models, forecast engagement, estimate growth trajectories, or train recommendation systems.

The benefit of using optimized datasets is that you spend less time cleaning and organizing the information. Instead, you can focus on testing algorithms, evaluating features, and building predictive tools. Whether you want to identify breakout artists, forecast streaming reach, or enhance A&R decision pipelines, the optimized datasets provide a starting point for experimentation.

Why a YouTube Dataset Matters for Music Professionals

YouTube remains one of the most influential platforms for audience discovery and artist exposure. Unlike purely audio‑based platforms, YouTube brings together search behavior, comments, shares, and long‑form viewing patterns. These signals help you understand fan communities in a more detailed way.

A YouTube dataset allows you to identify which artists are gaining attention, where their audience is located, and how engagement shifts over time. By comparing YouTube traction with other platforms, you can detect early indicators of momentum or find gaps in promotional strategy. For example, an artist with strong YouTube engagement but weaker Spotify numbers might benefit from focused distribution support or targeted advertising.

Using structured datasets also helps you avoid guesswork. Instead of relying on isolated performance metrics, you analyze patterns across thousands of data points. This allows you to form confident conclusions and design strategies backed by measurable evidence.

Conclusion

A well‑structured YouTube dataset is an important tool for anyone working in the modern music industry. With Viberate, you can build custom datasets, download them as CSV files, and apply them to everything from daily operations to machine‑learning projects. By combining flexibility with clean data structure, the platform supports professionals who want to work faster and make decisions based on reliable information. Whether you manage artists, program festivals, or analyze market behavior, the ability to customize and export datasets gives you a clearer, more precise view of the industry.

Source of music data: Viberate.com