Leveraging Data Engineering Techniques for Enhanced Business Intelligence

Ravi Kiran Pagidi; Rajas Paresh Kshir-sagar; Phanindra Kumar Kankanampati; Er. Aman Shrivastav,; Prof. (Dr) Punit Goel; Om Goel

doi:10.36676/urr.v9.i4.1392

Authors

Ravi Kiran Pagidi Independent Researcher, Jawaharlal Nehru Technological University, Hyderabad, India,
Rajas Paresh Kshir-sagar Independent Researcher, N.Y. University, Malad (W), Mumbai - 400064, Maharashtra, In-dia,
Phanindra Kumar Kankanampati Independent Researcher, Binghamton University, Miyrapur, Hyderabad, India 500049,
Er. Aman Shrivastav, Independent Researcher , ABESIT Engineering College , Gha-ziabad ,
Prof. (Dr) Punit Goel Research Supervisor , Maharaja Agrasen Himalayan Garhwal University, Uttarakhand,
Om Goel Independent Researcher, Abes Engineering College Ghaziabad,

DOI:

https://doi.org/10.36676/urr.v9.i4.1392

Keywords:

Data engineering, Business Intelligence, ETL pipelines, data warehousing

Abstract

In the rapidly evolving digital landscape, businesses are increasingly dependent on data to drive strategic decisions and maintain a competitive edge. The integration of data engineering techniques with Business Intelligence (BI) has become a key enabler for organizations to transform raw data into actionable insights. This paper explores how modern data engineering practices, such as Extract, Transform, Load (ETL) pipelines, data lakes, and data warehousing, play a pivotal role in enhancing BI systems. These practices streamline the process of data extraction from diverse sources, data transformation for consistency, and storage in optimized repositories to facilitate analysis.

Moreover, the implementation of real-time data processing and cloud-based solutions has revolutionized how businesses manage large-scale datasets, enabling the handling of both structured and unstructured data with increased speed and flexibility. The rise of big data technologies such as Hadoop, Spark, and NoSQL databases further extends the ability to perform complex queries and predictive analytics, empowering decision-makers with timely, accurate insights.

This abstract underscores the importance of building scalable, efficient data infrastructures through advanced data engineering methodologies. By integrating these techniques, businesses can ensure data quality, improve operational efficiency, and support comprehensive BI systems that drive innovation. Ultimately, this leads to more informed decision-making, enhanced customer experiences, and greater adaptability in the face of market changes. The effective combination of data engineering and BI is critical for leveraging data as a strategic asset in today’s competitive business environment.

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