Building Data Warehousing Solutions in Azure Synapse for Enhanced Business Insights

Afroz Shaik¹, Arth Dave², Vanitha Sivasankaran Balasubramaniam³, Prof. (Dr) MSR Prasad⁴, Prof. (Dr) Sandeep Kumar⁵ & Prof. (Dr) Sangeet⁶

¹Cleveland State University, Cleveland OH, USA, afrozseb1@gmail.com

²Scholar, Arizona State University, Arizona, USA

³Georgia State University, Goergia, Atlanta, GA, USA vanithab.msis@gmail.com

⁴Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vadeshawaram, A.P., India email2msr@gmail.com

⁵Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vadeshawaram, A.P., India er.sandeepsahratia@kluniversity.in

⁶Vashishtha, IIMT University, Meerut, India

Abstract

 This research explores the design and implementation of data warehousing solutions using Azure Synapse Analytics to drive enhanced business insights. With the growing need for organizations to process large volumes of data efficiently, Azure Synapse offers an integrated platform that combines big data and data warehousing capabilities, enabling real-time analytics and advanced reporting. This study delves into the architectural components of Azure Synapse, including data ingestion, transformation pipelines, and query optimization, to ensure seamless data integration across disparate sources. Furthermore, it highlights how the platform’s scalability and integration with Azure Data Lake and Power BI empower enterprises to derive actionable insights from complex datasets. By evaluating case studies and best practices, this research provides insights into the challenges and solutions in building robust data warehousing infrastructures. The findings emphasize the significance of leveraging cloud-based analytics solutions to support business decision-making, reduce operational costs, and enhance competitive advantage through data-driven strategies.

Keywords

Azure Synapse Analytics, data warehousing, business insights, cloud-based analytics, real-time analytics, data integration, query optimization, scalable infrastructure, big data processing, Power BI integration, advanced reporting, data transformation pipelines.

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