The exponential growth of big data generated from IoT devices, social media platforms, financial
systems, healthcare applications, and cloud infrastructures has significantly increased the demand
for efficient real-time analytics frameworks. Traditional batch processing systems are highly efficient
for handling massive historical datasets but suffer from high latency and delayed decision-making.
Conversely, stream processing techniques provide continuous low-latency computation for real-time
data analytics but introduce challenges in scalability, fault tolerance, and resource management.
This research presents a comprehensive comparative analysis between batch processing and stream
processing techniques for real-time analytics environments. The study evaluates Apache Hadoop,
Apache Spark Batch Processing, Apache Storm, Apache Flink, and Spark Streaming architectures
using parameters such as latency, throughput, fault tolerance, scalability, and processing efficiency.
Experimental evaluation demonstrates that stream processing frameworks significantly outperform
traditional batch systems in latency-sensitive applications such as fraud detection, IoT monitoring, and
real-time recommendation systems. However, batch processing frameworks continue to provide superior
performance for historical trend analysis and large-scale offline computation. The results indicate that
hybrid analytics architectures integrating both processing paradigms provide the most effective solution
for modern real-time big data ecosystems.
