Transforming Data Infrastructure for Scalable E-Commerce Analytics
Our client, a fast-growing B2B e-commerce platform operating across Southeast Asia and South Asia, needed a robust data warehouse to support their rapid expansion.
Their existing OLTP database lacked the structure and data quality needed to deliver reliable, scalable analytics. With growing data demands, they aimed to modernize their infrastructure to enable deeper business insights and decision-making agility.
View LiveB2B E-Commerce & Data Infrastructure
13 Weeks
- Data Warehouse Design
- ETL Framework Development
- Data Quality Improvement
- Nested JSON Data Handling
- SQL Script Development
- OLAP Cube Development
- Incremental Data Migration
- Performance Optimization
The Challenge
The client encountered critical limitations in their operational database setup. Their existing OLTP database could not support advanced analytics due to data quality issues, unstructured data, and limited reporting capabilities.
Poor Data Quality
Incorrect data types, orphan records, and inconsistent formatting reduced data reliability and made reporting difficult.
Complex Nested JSON Data
Unstructured nested JSON data hindered integration and required specialized handling to make it analytics-ready.
Lack of Dimensional Modeling
The existing schema lacked dimensions and facts, making it difficult to perform structured analytics and build scalable reporting.
Limited Reporting Capability
The client had no support for multidimensional analysis, limiting their ability to explore data across business units and decision-making needs.
Our Solution
OnPoint Insights re-engineered the client’s data infrastructure, delivering a clean, efficient data warehouse built for long-term scalability and advanced analytics.
The solution transformed operational data into a structured warehouse environment with improved quality, faster reporting, and multidimensional analysis capability.
Designed a robust ETL framework to clean, validate, and transform operational data into a dimensional warehouse schema.
Developed specialized SQL scripts to parse and flatten nested JSON fields for analytics readiness.
Standardized formats, resolved orphaned records, and validated field types for consistency and integrity.
Created OLAP cubes using SQL stored procedures to enable multidimensional analysis and fast query performance.
Adopted a phased, collaborative approach to tackle complex transformation logic and performance tuning.
Migrated data in phases with compatibility checks to ensure stability and minimize operational disruption.
Results & Impact
The modernized data infrastructure helped the client improve reporting reliability, strengthen data quality, and support scalable analytics across business functions.
Enhanced Reporting
Delivered timely, reliable reports and dashboards with advanced analytical depth.
High Data Integrity
Reduced orphan data and inconsistencies, improving trust in data-driven decisions.
Efficient OLAP Analysis
Enabled drilldowns, slicing, and performance-optimized reporting across business units.
Operational Efficiency
Streamlined processes and reduced maintenance effort through automation and clean architecture.
Scalability
Designed a future-proof system ready to accommodate growing data and evolving business requirements.
Empowered Decision-Making
Provided business users with intuitive tools and real-time visibility into metrics.
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