The rising demand for real-time data analytics in domains such as the Internet of Things (IoT) and telecommunications necessitates hybrid big data architectures that seamlessly combine batch and stream processing. This study investigates the integration of Hadoop with real-time streaming engines, specifically Apache Storm and Apache Flink, to address the challenges of low-latency analytics within traditional big data frameworks. We analyze performance trade-offs, latency mitigation techniques, and fault tolerance mechanisms involved in such hybrid deployments. Through benchmarking and architectural evaluation, the research identifies key design considerations, including pipeline optimization and efficient resource management strategies that support concurrent batch and real-time workloads. Empirical insights from IoT and telecom use cases illustrate the effectiveness of integrating Hadoop's scalable storage with the high-throughput, low-latency processing capabilities of modern stream engines. The findings affirm the practicality and performance benefits of adopting a unified analytics ecosystem for real-time data-driven decision-making.