Real-Time Data Processing and Big Data Analytics: A Comparative Study of Modern Platforms

The exponential growth of data has made real-time data processing and big data analytics indispensable for extracting valuable insights and supporting decision-making across various sectors. This chapter presents a comparative study of modern platforms, categorized into batch processing, stream analytics, and interactive analytics, each with unique capabilities and use cases. Key platforms such as Apache Kafka, Apache Storm, Apache Flink, and cloud-based solutions like Amazon Kinesis and Google Cloud Dataflow are evaluated based on critical metrics including data ingestion, latency, scalability, state management, and fault tolerance. The study highlights the strengths and weaknesses of each platform, providing a detailed analysis of their performance in handling high-throughput data streams and complex event processing. Additionally, the chapter explores the integration capabilities of these platforms with other big data tools and their deployment models, offering a comprehensive framework for selecting the most suitable technology. The evolving landscape of big data technologies and their impact on industries such as finance, healthcare, and telecommunications is also discussed, emphasizing the importance of real-time analytics in driving innovation and operational efficiencies.