Big Data Analytics: Architectures, Tools, and Applications in Modern Enterprise Environments
Keywords:
Big Data Analytics, Apache Hadoop, Data ArchitectureAbstract
The exponential growth of digital data generated by social media platforms, IoT devices, enterprise systems,
and scientific instruments has created both challenges and opportunities for data-driven decision-making.
Big data analytics encompasses the technologies and methodologies for processing, analyzing, and deriving
insights from datasets characterized by high volume, velocity, variety, veracity, and value. This paper
presents a comprehensive examination of big data architectures, processing frameworks, and analytical
tools, with emphasis on their practical applications in enterprise environments. We analyze batch processing
frameworks including Apache Hadoop and Spark, stream processing systems such as Apache Kafka and
Flink, and NoSQL database ecosystems designed for scalable data storage. Case studies from healthcare,
retail, and telecommunications sectors illustrate the business value generated through big data analytics
implementations. The paper further addresses emerging challenges including data governance, privacy
compliance, real-time analytics requirements, and the integration of machine learning pipelines within big
data architectures.
References
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70), 1-4.
Zaharia, M., et al. (2016). Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11), 56-65.
Marz, N., & Warren, J. (2015). Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Manning Publications.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
Armbrust, M., et al. (2020). Delta Lake: High-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411-3424.
