The Modern CPG Data Stack: Building End-To-End Analytics on Azure, Snowflake, And Dbt

Аннотация

OPEN ACCESS Consumer Packaged Goods (CPG) firms encounter SUBMITED 11 June 2025 ACCEPTED 28 June 2025 distinct obstacles in handling the extensive data PUBLISHED 11 July 2025 produced from sales, supply chains, customer VOLUME Vol.07 Issue 07 2025 interactions, and market patterns. To tackle these CITATION obstacles, organizations are progressively implementing Supriya Gandhari. (2025). The Modern CPG Data Stack: Building End-To- End Analytics on Azure, Snowflake, And Dbt. The American Journal of a contemporary data stack. This document examines Interdisciplinary Innovations and Research, 7(07), 91–100. how using Microsoft Azure, Snowflake, and DBT https://doi.org/10.37547/tajiir/Volume07Issue07-08 technologies can create a comprehensive analytics stack COPYRIGHT that improves scalability, data transformation, and © 2025 Original content from this work may be used under the terms decision-making processes. The paper outlines the of the creative commons attributes 4.0 License. technical framework, best practices, and real-world examples that showcase the implementation of this modern CPG data stack. By utilizing cloud-based solutions, businesses can enhance operational efficiency, automate data processes, and acquire more profound insights. We will investigate performance metrics, security risks, cost-efficiency methods, and forthcoming trends that influence the advancement of CPG analytics. Keywords: Modern Data Stack, DBT, Snowflake, Azure Data Factory, Data Engineering, Cloud Data Platforms, ETL Pipelines, Data Transformation, Data Orchestration, Data Integration, Real Time Analytics, Cloud Native Solutions 1. INTRODUCTION In recent years, the swift digital transformation of companies has resulted in a significant increase in data volume and complexity. Conventional data management systems have found it challenging to adapt to these changes, leading to the emergence of a more adaptable The American Journal of Interdisciplinary Innovations and Research 91 https://www.theamericanjournals.com/index.php/tajiir The American Journal of Interdisciplinary Innovations and Research and scalable framework— often termed the Modern organizations extract value from data and is especially Data Stack (MDS). The MDS encompasses a collection of impactful in data-centric industries such as e-commerce, cloud-native tools that work together to facilitate finance, healthcare, and technology. A graphical effective data ingestion, storage, transformation, representation of modern data stack has been shown in analysis, and governance [1]. This shift is altering the way Fig. 1. Fig. 1. Modern Data Stack Architecture The Consumer-Packaged Goods (CPG) sector produces effectiveness. This research is particularly pertinent at a vast amounts of data from various sources such as retail time when organizations are reassessing their data sales, online shopping platforms, logistics systems, and strategies due to increasing data privacy regulations, the customer interaction points. Traditionally, businesses necessity for real-time analytics, and the rise of third- depended on outdated systems that were isolated, party SaaS applications. sluggish, and unable to manage contemporary data The main goals of this paper are: requirements [2]. Nevertheless, due to innovations in cloud technology, data storage, and transformation To define and articulate the components of the modern tools, companies are now transitioning to a completely data stack. To investigate the primary factors driving unified analytics framework. the transition from traditional data systems to contemporary architecture. Even with the growing utilization of the modern data stack, there is a notable absence of thorough academic To assess the practical consequences and effects research into its architecture, advantages, and obstacles. on organizations resulting from the implementation Most available literature centers on specific components of a modern data stack. (such as cloud data warehouses or ELT tools) rather than To emphasize ongoing challenges and future avenues for providing a comprehensive perspective on the entire research and practice. stack [1]. Consequently, the practical integration and strategic deployment of MDS components are poorly This study offers a structured synthesis of existing tools understood in many businesses. This paper seeks to and practices within modern data infrastructures, address this gap by providing an in-depth analysis of the backed by both industry examples and academic modern data stack and comparing it to traditional data insights. systems regarding scalability, performance, and cost- The American Journal of Interdisciplinary Innovations and Research 92 https://www.theamericanjournals.com/index.php/tajiir The American Journal of Interdisciplinary Innovations and Research Microsoft Azure offers a cloud-based infrastructure that tools and data sources. allows for efficient storage, computation, and analytics. By utilizing Azure for infrastr