INVESTIGATING SCALABLE ANALYTICAL FRAMEWORKS FOR REAL TIME INSIGHTS IN BIG DATA ENVIRONMENTS
Abstract
As data volumes continue to expand exponentially across industries, the demand for scalable analytical frameworks capable of delivering real-time insights in big data environments has surged. This paper investigates state-of-the-art scalable architectures and processing paradigms such as Apache Spark, Flink, Kafka Streams. It explores their strengths and limitations, real-time analytics capabilities, and adaptability in varied domains. A comparative analysis supported by charts and tables provides clarity on their practical efficiencies. The literature review critically evaluates foundational research, identifying gaps and developments that shaped the current technological landscape.