The proliferation of data in the digital age has led to the emergence of Big Data, with data streams being a significant component of this phenomenon. Managing and extracting meaningful insights from these data streams in real-time is a critical challenge in various domains, including finance, healthcare, the Internet of Things (IoT), and social media. This paper provides an overview of techniques and applications for real-time query processing in Big Data streams, aiming to bridge the gap between the volume and velocity of data and the need for timely decision-making. In this paper, we first explore the characteristics of Big Data streams, highlighting their continuous, high-velocity nature and the challenges they pose to traditional batch processing approaches. We then delve into the techniques and technologies that enable real-time query processing in such environments. These include stream processing frameworks, complex event processing (CEP) systems, and various machine learning algorithms designed for streaming data. As Big Data streams continue to grow in importance, understanding and harnessing the power of real-time query processing becomes increasingly vital. This paper serves as a comprehensive guide for researchers, data scientists, and practitioners interested in the techniques and applications of processing and analyzing Big Data streams in real time, fostering innovation and informed decision-making in the ever-evolving data landscape.