Leveraging Azure Data Lake for Efficient Data Processing in Telematics
DOI:
https://doi.org/10.36676/urr.v9.i4.1397Keywords:
Azure Data Lake, telematics, data processing, scalability, real-time analytics, data storage, predictive analyticsAbstract
In the telematics industry, the continuous generation of large volumes of data presents significant challenges in terms of storage, processing, and analysis. Azure Data Lake, a scalable and secure data storage solution, offers an efficient platform to handle these massive datasets. This paper explores the application of Azure Data Lake in telematics for efficient data processing, focusing on its capacity to store vast amounts of structured and unstructured data while providing seamless integration with various analytics tools. By leveraging Azure Data Lake, organizations can enhance their data processing capabilities, improve decision-making, and reduce operational costs.
The study investigates how Azure Data Lake simplifies data management through its high availability and accessibility, allowing businesses to manage data from multiple sources with minimal complexity. Furthermore, the integration with Azure services like Azure Data Factory and Azure Databricks facilitates advanced analytics, enabling real-time insights and predictive analytics, which are crucial for the telematics sector. The findings suggest that adopting Azure Data Lake improves data processing efficiency, enhances scalability, and supports the development of innovative telematics applications such as fleet management and vehicle monitoring systems. The paper concludes by highlighting the potential of Azure Data Lake to revolutionize the telematics industry by enabling more agile and data-driven operations.
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