Working time:
2025 – ongoing
Industry:
Retail
The service:
Data Architecture & Analytics
Overview
The client is a premium regional grocery chain known for its curated selection of high-quality food and beverage products. With a growing presence in suburban and urban neighborhoods, the brand is built on personalized service, specialty assortments, and a strong commitment to local sourcing.
To stay competitive in a fast-evolving retail environment, the company needed to modernize how it managed and used data. Challenges like inconsistent reporting, limited real-time visibility, and siloed operations made it difficult to scale insights and support data-driven decisions across merchandising, store operations, and marketing.
We partnered with the client to build a cloud-native, fully automated data platform on AWS—designed to improve inventory accuracy, unlock customer insights, and establish a foundation for future AI-driven innovation.
Challenge
Siloed Operational Data
Sales, inventory, and supplier data were dispersed across different systems. This fragmentation made it difficult to track product performance across store locations or measure the success of new promotions and product launches.
Inflexible Reporting Processes
Most reporting relied on end-of-day batch data, with limited ability to view store performance or inventory trends in real time. Teams were slow to act on supply issues or customer demand signals.
Limited Scalability and Visibility
The company’s data infrastructure was not designed to support expansion. As new locations opened and operations became more complex, scaling the existing reporting tools became costly and inefficient.
Rising Operational Costs
Manual ETL processes, underutilized compute resources, and on-prem storage systems contributed to rising data platform costs—without delivering proportional business value.
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Solution
Our team designed and implemented a flexible AWS-based data architecture tailored to the client’s retail operations and growth objectives. The core solution included:
Centralized Data Lake on Amazon S3
All point-of-sale, inventory, pricing, and supplier data was consolidated into a structured Amazon S3-based data lake. Using AWS Glue, we created standardized data catalogs and automated ETL pipelines to streamline ingestion and transformation.
Real-Time Data Pipelines with Amazon Kinesis
We implemented real-time data pipelines using Amazon Kinesis to capture live transaction and stock-level events from all store locations. This enabled dynamic dashboards that reflect up-to-the-minute store activity, including sales spikes and low-stock alerts.
Scalable Analytics with Amazon Redshift
We deployed an analytical warehouse on Amazon Redshift, giving business users and analysts self-serve access to curated datasets. Redshift supports customer segmentation, campaign performance reporting, and cross-store trend analysis—all from a single source of truth.
Secure Access with AWS Lake Formation
To ensure data governance and security, we used AWS Lake Formation to define role-based access policies and enforce compliance across sensitive business and customer data. This allowed for secure collaboration between operations, finance, and marketing teams.
Cost Optimization and Lifecycle Management
With intelligent S3 lifecycle rules, we reduced long-term storage costs by transitioning infrequently accessed data to lower-cost tiers. We also implemented usage tracking and cost tagging to monitor spend across departments and data workloads.
Blog
5 min to read
Data Infrastructure 101: Building for Scalability and Security
Outcomes
Inventory Visibility in Real Time
Store managers and inventory teams now have immediate visibility into stock levels across locations, allowing them to react faster to trends and avoid product shortages—particularly in high-demand categories like fresh food and beverages.
50% Faster Business Insights
Reporting timelines were reduced by half, enabling teams to measure the impact of pricing changes, supplier promotions, and product assortment decisions within hours instead of days.
65% Reduction in Data Management Overhead
Manual data processing has been replaced by fully automated pipelines, freeing up technical teams and reducing dependency on legacy reporting systems.
Single Source of Truth Across Business Functions
All stakeholders now operate from a unified data foundation—supporting accurate, consistent decision-making across merchandising, supply chain, and executive reporting.
AI-Ready Infrastructure
The client is now equipped to experiment with machine learning and GenAI solutions, including dynamic product recommendations, demand forecasting, and personalized customer experiences—without needing to re-architect their data foundation.
