IJBTT

Real-Time Marketing Optimization through Scalable Telemetry Data Engineering: A Framework for Enhanced Engagement and ROI
	© 2025 by IJCTT Journal
	Volume-73 Issue-1
	Year of Publication : 2025
	Authors : Srinivasa Rao Nelluri, KrishnaMurthy Poluri
	DOI :  10.14445/22312803/IJCTT-V73I1P103

How to Cite?

Srinivasa Rao Nelluri, KrishnaMurthy Poluri, "Real-Time Marketing Optimization through Scalable Telemetry Data Engineering: A Framework for Enhanced Engagement and ROI," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 26-31, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P103

Abstract
In an increasingly data-driven landscape, organizations seek to leverage telemetry data to unlock valuable marketing insights that drive customer engagement, personalization, and retention strategies. Telemetry data—automatically generated, time-stamped information from customer interactions, product usage, and digital touchpoints—provides a good source of real time behavioral data. However, effectively capturing, processing, and analyzing this data at scale presents significant challenges in terms of data ingestion, storage, processing, and analytical workflows. This presentation explores a comprehensive and scalable approach to telemetry data engineering designed to transform vast amounts of raw data into actionable insights for marketing teams. We begin by outlining the unique characteristics of telemetry data and discussing its potential to enhance marketing insights, particularly in areas such as customer segmentation, predictive analytics, personalization, and engagement tracking. We then present an end-to-end architecture for telemetry data pipelines, from data ingestion to advanced analytics. The architecture employs a combination of modern big data technologies, including stream processing frameworks (Apache Kafka, Apache Flink) [11][14], distributed storage systems (Apache Hadoop Distributed File System (HDFS) [12], cloud storage solutions), and analytics platforms (Apache Spark, Delta Lake). This setup ensures both real-time and batch processing capabilities, enabling marketing teams to access up-to-the-minute insights as well as long-term trend analyses.

Keywords
Apache kafka, Apache Hadoop Distributed File System (HDFS), Apache flink, Delta lake, Snowflake.

Reference

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