We want to fight against fake streams on Spotify. Every second artist on Spotify is faking their streams, and we don't want this to dominate the music industry anymore. So, we developed this site to promote fairness and transparency.
We specifically get raw data from Spotify, process it, and display it legibly on the site. We take only a few parameters and aggregate them 1:1, making it easy for users to understand. This processing involves sophisticated algorithms and artificial intelligence to accurately identify patterns indicative of fake streams.
They could be dead accounts on Spotify or unclassified streams. This means they are only declared by us as well as Spotify as 50% fake streams, but it could also be a real person. The classification is not always black and white.
We incur costs with our server provider due to the high automated data processing. We use a lot of CPU and RAM capacity every time we process the raw data, which necessitates a fee to cover these expenses.
No, we will only send you a short email to notify you that the processing is complete. Your email address will not be shared with any third parties.
Just click on "Contact" and we will try to answer your questions as quickly as possible.
Yes, you can. There is a link provided in the PDF report where you can download the raw data. Note that the file size is limited to a maximum of 2GB, and the download will be aborted if there are too many plays.
Our detection process relies on advanced algorithms and AI. We analyze patterns in the data to identify anomalies that indicate fake streams. This includes examining user behaviors, play counts, and other metrics to ensure accuracy and reliability in our findings.
Absolutely. We take data security very seriously and employ robust encryption methods to protect your information throughout the entire processing cycle.
Our algorithms are continuously refined to improve accuracy. While no system is perfect, we strive to provide the most reliable results possible based on the data available.
The data on check-stream.com originates from various official APIs provided by Spotify and three specific distributors who collaborate with Spotify. Additionally, we leverage data from other sources to obtain a comprehensive view of streaming activities. This comprehensive data acquisition allows us to provide accurate analyses of stream quality and identify potentially fraudulent activities.
We utilize Apache Kafka as a central platform for processing streaming data. Apache Kafka enables us to perform scalable, fault-tolerant, and real-time data processing. Here are key aspects of how we use Apache Kafka in our system:
- **Stream Processing:** Apache Kafka allows us to process large volumes of streaming data in real-time. This is crucial for performing fast and efficient analysis of millions of streams played on Spotify daily.
- **Scalability:** Due to Apache Kafka's distributed architecture, we can scale processing capacity as needed. This means we can handle fluctuations in data traffic, whether during peak times or when processing large volumes of data.
- **Fault Tolerance:** Apache Kafka provides built-in fault tolerance mechanisms to ensure no data loss, even if individual nodes or components fail. This ensures the reliability of our services and continuity of data processing.
- **Integration with Other Systems:** We also use Apache Kafka for seamless integration with other components of our system, including databases, analytics tools, and API services. These integrations allow us to effectively store, analyze, and generate real-time insights from the data.
