This paper presents a comprehensive case analysis of Flipkart's big data analytics strategy, examined through the core theoretical and technical frameworks of the discipline. Flipkart, India's largest e-commerce platform with over 500 million registered users and a peak processing capacity of eight million orders per day, generates three to five petabytes of data daily—a scale that necessitates a sophisticated, multi-layered data architecture. Drawing on engineering disclosures, industry benchmarking data, and a proprietary product catalogue dataset of approximately 20,000 records, this study maps Flipkart's operational practices against the five Vs of big data (volume, velocity, variety, veracity, and value), the Hadoop and Apache Spark ecosystems, SQL and NoSQL paradigms, OLAP and OLTP processing models, data warehousing and data lake architectures, ETL pipelines, and all four types of analytics maturity. Three embedded case studies—covering Big Billion Days sale optimisation, the personalised recommendation engine, and Ekart last-mile logistics analytics—illustrate how abstract technical concepts translate into measurable business outcomes, including a 30% reduction in stockouts, a 91% first-attempt delivery success rate, and recommendation-driven revenue contribution of approximately 15% of total gross merchandise value. A structured peer comparison with Amazon India, Myntra, and Meesho benchmarks Flipkart's capabilities against industry leaders and identifies strategic gaps. The paper concludes with seven prioritised recommendations—spanning data lakehouse migration, federated machine learning, graph-based fraud detection, causal inference pricing, vernacular natural language processing, data governance, and edge analytics—to strengthen Flipkart's competitive data position.

Keywords: Big data, E-commerce analytics, Hadoop, Apache Spark, Flipkart, Recommendation systems, Demand Forecasting, Data lake, ETL pipeline, cluster computing, fault tolerance, OLAP, NoSQL, machine learning, data governance