Development of a Cloud-Native Big Data Analytics Platform for Large-Scale Inventory and Operations Management

The client faces significant challenges in managing vast amounts of operational and inventory data sourced from multiple departments and external systems. Existing on-premise solutions hinder scalability, reliability, and cost-efficiency, hampering effective analytics and operational insights. The client seeks to migrate to a scalable, cloud-based platform to centralize data management, reduce operational overhead, and enhance data processing capabilities.

A large-scale industrial supply company with extensive inventory, logistics, and operational data managing over millions of products and serving a vast customer base.

• Migrate existing on-premise data infrastructure to a scalable cloud environment to improve reliability and reduce costs.
• Integrate over 100 diverse data sources into a unified data platform supporting daily and historical data loads.
• Implement automated data extraction and processing workflows to minimize manual intervention and operational overhead.
• Enable advanced predictive analytics for inventory-related costs and operational metrics.
• Design a cloud-neutral architecture allowing flexibility in cloud provider choice, ensuring vendor lock-in avoidance.
• Support growth by managing data volume increase to terabytes of data and beyond.
• Deliver a platform capable of real-time data access and analytics for different departments.

• Multi-source data ingestion system supporting various data formats and daily/historical loads.
• Automated data pipeline orchestration with scheduling and error handling (e.g., using workflow automation tools).
• Data storage solutions supporting scalable, cost-efficient data warehousing (e.g., cloud-native data warehouses).
• Data deduplication mechanisms to prevent redundant data processing.
• Support for cloud-agnostic infrastructure deployment using tools like Terraform.
• Integration with existing reporting tools like Tableau and internal dashboards.
• Real-time data processing and query capabilities for operational insights.

• Enterprise resource planning (ERP) systems such as SAP
• Relational databases (MS SQL, Oracle)
• Data reporting and visualization tools (e.g., Tableau, internal dashboards)
• Existing data warehouses (e.g., Teradata or similar)

• Scalability to handle data volumes reaching terabytes and beyond
• High availability with minimal downtime
• Reliable data ingestion with duplicate prevention
• Flexible cloud deployment supporting multi-cloud strategies
• Secure access and data privacy compliance

The implementation of a unified, cloud-native big data analytics platform is expected to significantly reduce operational and infrastructure costs, streamline data management processes, and improve data accessibility across departments. It will enable advanced predictive analytics, leading to better inventory cost forecasting and operational decision-making. Scalability and cloud neutrality will ensure future growth and flexibility in cloud provider choices, supporting sustained business agility.