Scalable Data Pipelines for Real-Time Analytics: Innovations in Streaming Data Architectures
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I1P102Keywords:
Real-Time Analytics, Streaming Data, Apache Flink, Spark Streaming, Apache Kafka, Data Pipelines, Low Latency, Big Data Processing, State Management, Data IngestionAbstract
Real-time analytics has become a critical component in various industries, from finance to healthcare, enabling organizations to make data-driven decisions with minimal latency. However, the rapid growth in data volume and velocity poses significant challenges for traditional data processing systems. This paper explores the latest innovations in streaming data architectures designed to address these challenges. We discuss the evolution of data pipelines, the key components of scalable real-time data processing systems, and the algorithms that enable efficient data streaming. We also present case studies and empirical evaluations to demonstrate the effectiveness of these architectures in real-world scenarios
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