Unified Data Infrastructure and Automated Reporting System for Cost Optimization in Cloud-Integrated Tech Operations

The client faces high expenses due to on-premise infrastructure and fragmented data management, leading to inefficient data accessibility and high manual effort in generating client reports. Existing processes rely on siloed data sources and paid third-party tools, resulting in costly, time-consuming operations that hinder scalability and timely insights.

A large, global tech enterprise managing extensive hardware and cloud infrastructure across multiple data centers worldwide, offering managed cloud hosting solutions and data-driven services.

• Migrate existing on-premise data infrastructure to a centralized cloud-based data platform to improve data accessibility and scalability.
• Automate the extraction, transformation, and loading (ELT) processes to reduce manual effort and improve data accuracy.
• Implement a unified data warehouse to consolidate over 70 data sources, 4 data repositories, and data lakes into a single cloud environment.
• Reduce operational costs by decommissioning redundant hardware and optimizing cloud resources.
• Streamline client reporting workflows to eliminate nearly 17,000 work hours annually through automation.
• Enhance data quality by identifying and correcting anomalies in the data pipeline.

• Centralized data warehouse integrating multiple data sources and data lakes in a scalable cloud environment.
• Automated data pipeline utilizing ELT architecture to seamlessly extract data from various sources, load into the warehouse, and perform transformations post-load.
• Real-time data ingestion from multiple endpoints such as servers, network devices, and hardware sensors.
• Analytics and reporting modules that automatically generate client-facing monthly service reports from raw operational data.
• Data anomaly detection and cleansing features to ensure high data quality and reliability.
• Decommissioning of legacy servers and infrastructure through cloud migration to achieve cost savings.

• Various hardware vendors (e.g., Cisco, IBM) for data collection
• Kafka for streaming data ingestion
• Third-party tools for initial data aggregation (to be replaced by in-house solutions)

• System scalability to handle data from over 40 global data centers with hundreds of thousands of equipment instances.
• Data processing latency optimized for near real-time reporting cycles, with minimal delay between data ingestion and report generation.
• High data throughput capacity to support continuous data streams from multiple endpoints.
• Robust data security and compliance measures aligned with industry standards.
• High availability and fault tolerance to ensure consistent report delivery.

The project aims to significantly reduce operational expenses by migrating legacy infrastructure and automating manual reporting processes. Expected benefits include cost savings exceeding $1 million annually, elimination of approximately 17,000 work hours per year, improved data quality and accessibility, and enhanced scalability to support evolving business needs.