Challenge

Outdated monolithic applications for consumer behavior and retailer analysis. Poor execution performance and excessive memory consumption due to outdated methods. Limitations with MS SQL as the primary database or data warehouse. Optimizing memory consumption caused by the Apriori algorithm. Writing efficient stored procedures for MS SQL and Snowflake platforms. Addressing complex analysis needs like market basket analysis, clustering, and cluster profiling.

Approach

Implemented microservices architecture in both applications to leverage improved scalability and flexibility. Captured customer perspectives using market basket analysis algorithms and machine learning techniques. Employed advanced data processing techniques for retail performance metrics where utilized clustering algorithms and cluster profiling to generate report-ready data. Transformed applications into high-performing solutions for actionable insights. These applications were designed so that they can leverage both Snowflake as well as MSSQL based on client needs.

Outcome

Processes and operations were streamlined, resulting in faster execution times and reduced latency. Snowflake's architecture resulted in seamless scaling, accommodating growing data volumes and user loads without compromising performance. Tasks were optimized and automated, leading to reduced manual efforts, fewer errors, and higher productivity. Added features to existing applications, yielding better outcomes.