The exponential growth in data production worldwide has stimulated the development of heterogeneous frameworks to make it possible to handle and process vast, diverse volumes of data. These frames are divided into three main categories: batch processing, stream analytics, and interactive analytics. These include batch processing, which is highly efficient in dealing with large-scale computations but falls short of many real-time qualities; stream analytics, which makes real-time processing of data possible but is also very complex to implement; and interactive analytics, which facilitates dynamic exploration of data but at the cost of being resource-intensive. This paper draws a comparison among these frameworks on several key criteria: data ingestion, latency, scalability, and cost. Our findings help guide researchers and practitioners toward the most appropriate framework in alignment with their specific needs for big data and real-time processing under their unique requirements for speed, data volume, and processing complexity.