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Traditional analytics is a conventional process of analyzing a batch of data sets collected over time. Usually, processing of the data in conventional processes occurs offline. This method involves longer processing times and delays in getting meaningful insights from the data set. Decisions are taken in retrospect as the data is processed offline and it is similar to work with historical data. Real-time analytics is a discipline in which analytics is completed as soon as new data arrives in the database. This method provides rapid insights and allows stakeholders to make timely decisions. This enables organizations to quickly respond to dynamically changing conditions, seize opportunities, and mitigate risk more effectively. A key distinction between traditional and real-time analytics is in terms of scalability. In the conventional approach, it becomes complicated to accommodate sudden data surges and the required volume to be processed and it will call for expensive resource deployment. Real-time analytics platforms are designed for scalability and these platforms can dynamically utilize resources to accommodate sudden surges in data processing demand, making the analysis consistent and reliable. Common challenges that data engineers face in real-time data processing are: a.) Handling large volumes of data: Analytics would yield an optimum result if a large set of data is processed for any given objective. Processing this high-volume data sometimes creates a bottleneck for engineers as they try to figure out how to manage and make use of this large amount of data. b.) Managing high variety of data: Usually every data source does not always follow a standard template hence data collected from these sources would have a high variety of structures, formats and it becomes difficult to process and transform this unorganized data and make sense of it for the stakeholder c.) Quality of data: There is a saying that “garbage in is garbage out”. Data will only be useful to derive insight s if that data is accurate. It is imperative that while processing inaccuracies present in the data are identified and reported for the user for effective decision-making. Identifying such noise in real time is also a key challenge for real-time analytics. d.) Infrastructure requirement: Real-time analytics requires processing complex and high-volume data as soon as it enters the database. This would require creating and managing such advanced infrastructure that can handle such kind of speed and velocity of data processing. The cost of establishing such a level of infrastructure would be very high. e.) To maintain low latency and high performance: Real-time analytics aims to provide quick meaningful insights and analysis to the user. This can be a key challenge to maintain such low latency and quality of insights in real-time by minimizing processing delays, optimizing data pipelines, and rapid query performance.