Tools and techniques for real-time data processing: A review

Real-time data processing is an essential component in the modern data landscape, where vast amounts of data are generated continuously from various sources such as Internet of Things devices, social media, financial transactions, and manufacturing systems. Unlike traditional batch processing methods that analyse data in intervals, real-time data processing enables the continuous intake, manipulation, and analysis of data within milliseconds of generation. This capability is critical for applications requiring instant insights and rapid decision-making, including fraud detection, predictive maintenance, real-time analytics, and autonomous operations. This paper reviews the tools and techniques that have revolutionized real-time data processing, with a focus on cutting-edge platforms such as Apache Kafka and Apache Flink, as well as cloud-native solutions. These technologies offer scalable and fault-tolerant systems capable of managing high-volume data streams while ensuring low latency and data consistency. Apache Kafka provides a highly scalable distributed messaging system, while Apache Flink combines stateful and stateless processing to support complex event-driven applications. This review highlights the. This paper reviews key techniques and tools used in real-time data processing, including stream processing, complex event processing, in-memory computing, micro-batching, and real-time dashboards. In addition, it highlights advancements in real-time data processing frameworks, their capabilities, and their impact on modern business applications. Additionally, the paper explores various tools used in real-time data processing, including Apache Kafka for data ingestion, Apache Flink and Spark Streaming for stream processing, Redis and Apache Druid for real-time storage, and Grafana and Kibana for data visualization. By examining these techniques and tools, this paper highlights the importance of real-time data processing in enabling businesses to make data-driven decisions with minimal latency, ultimately gaining a competitive edge in the rapidly evolving digital world.