International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Modern Business Analytics through Cloud-Native Architectures: A Comprehensive Study Using AWS Data Lake and Machine Learning Services Shiraz Anas Quadri Syed1, Shayesta Syeda2 1AWS Solutions Architect, Los Angeles, California, USA 2Student, Business Analytics, Hyderabad, Telangana, India
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Abstract - With the fast-paced digitalization of
evolved from a back-office activity to an organizational competency that is foundational to competing on excellence or new customer-centric services. This is why the discipline known as Business Analytics has become so prominent within today's enterprise, addressing the need for raw data to evolve into actionable insight and predictive intelligence. Traditional analytics infrastructure usually relying on an onpremises database, fixed compute capacity, and batch-style processing: The demand for real-time insights, cost efficiency and massive machine learning is moving so fast backend can hardly keep up. As it turns out, these pressures have pushed the trend towards cloud-native architectures (with elasticity, distributed processing and machine learning services built-in) even further along. Amazon Web Services (AWS) offers a robust analytics eco-system capable to store data, automate ETL, govern processes, visualize outputs and predict final outcomes all under one scalable environment.
contemporary businesses, data is not only everywhere but it is key to informed decision making. As businesses grow, the demand for more scalable, flexible and intelligent analytics infrastructures have increased, and legacy on-premises systems have become outdated. This paper investigates how cloud-native architectures based on platforms like Amazon Web Services (AWS) are disrupting the world of Business Analytics through unlimited performance data lakes, automated data engineering pipelines or sophisticated machine learning capabilities. Based on a fusion of the business analytics frameworks and cloud engineering design principles, this paper analyses how AWS offerings (Amazon S3, AWS Glue, Lake Formation, Redshift, Athena and SageMaker) collectively fulfil tasks for descriptive, diagnostic, predictive and prescriptive analytics. It combines two separate areas of expertise - data-driven business analysis and cloud-native architecture design – created together, but remotely.
This study is investigating the confluence of business analytics and cloud-native architecture, specifically how AWS allows businesses to transition from Traditional BI (Business Intelligence) to advanced decision intelligence. While this compendium represents two disparate professional perspectives, one based on business analytics theory and the other based in cloud engineering the collaboration was remote, blending domain expertise asynchronously. This is consistent with the real-world scenario of independent projects involving cross-discipline collaborations between data team, architects and analysts frequently work in separate geographical locations.
The study’s results signify that AWS’s integrated analytics environment powers more scalable and cost-effective data processing, delivers faster insights, democratizes machine learning, and fosters decision intelligence across an organization. Together, the findings of our combined research, suggest that cloud-native architectures for business analytics are a key step change in using data to drive competitive advantage and provide organizations with an infrastructure that is commercially mature as well as analytically sophisticated.
Contemporary business analytics frameworks generally consist of four foundational analytic types including descriptive, diagnostic, predictive and prescriptive. Data ingestion and organization must be robust at each layer with manageability of access and scalable compute. AWS helps these organizations via services like Amazon S3 for data lake storage, AWS Glue for serverless ETL, Amazon Redshift for petabyte-scale warehousing, Amazon Athena for interactive SQL querying and Amazon QuickSight for visualization. Advanced models use Amazon SageMaker, Amazon Forecast, Amazon Comprehend and Amazon Personalize to put machine learning at scale into action.
Key Words: Business Analytics, Cloud-Native Architecture, AWS Data Lake, Machine Learning, RealTime Analytics, Large Language Models (LLMs), Data Engineering, Predictive Analytics
1. INTRODUCTION Digital ecosystems are rapidly transforming the way in which organizations capture, store and analyze data. So, the explosive growth of e-commerce, mobile apps, Internet of Things devices and cloud-based platforms have driven unimaginable amounts of structured and unstructured data. For businesses competing in this environment, analytics has
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