The technological revolution, the huge sharing of data via social networks, web and mobile applications and IoT devices are generating a huge volume of data every day, commonly referred to as “Big Data”. To cope with Big Data and the challenges associated with their specific features, the last decade, a multitude of technologies and platforms have emerged to harness their potential. The community is still seeking a comprehensive and up-to-date comparative study of these tools. Such an experimentally-derived study is essential for enabling informed decision-making, fostering innovation, and ensuring that organizations can make the best choices when implementing Big Data solutions. In this paper, a multi-purpose experimental study was conducted. The primary objective is to provide an overview of today’s most popular Big Data tools, and to evaluate their interoperability. The second is to test performance by varying different technical constraints. The aim of these tests is twofold: i) To compare the resource consumption requirements of these tools, ii) To evaluate the impact of resource variation of one tool on the performance of another one in the same Big Data pipeline.