Network providers for the most part lack insight into YouTube end-users' QoE because of traffic encryption. Given these conditions, network providers usually cannot detect KPIs like initial delay, stalling events or quality switches from the traffic passing through their networks. At the Faculty of Electrical Engineering and Computing, University of Zagreb, a lot of experiments were performed to collect both application and network data, and develop machine learning-based solutions for estimating video streaming QoE/KPIs from encrypted traffic. Those experiments were performed in a controlled or a real environment. During dataset analysis, where a dataset is a collection of experiments, there was a lot of repetitive work. Another problem was related to storing data in an organized way, and lack of tools for efficient access, merging, and filtering of the data. As a result, the aim of this thesis was to develop a web application that could solve listed problems. The time to analyze datasets will be significantly reduced with developed application Auto-A Storage since all processing of the data will be done automatically.the This application will provide a customized solution for storage for all measurement data. Some of the main features of Auto-A Storage are storing, editing, downloading, and filtering of datasets, as well as downloading generated ML-ready data. It has implemented authentication and authorization so nobody can access to Auto-A Storage without application administrators' permission. The front-end of Auto-A Storage was implemented in React, a JavaScript library, while the back-end was implemented in Spring Boot, a Java framework.