Enterprise Big Data Platform Migration to Cloud-Based Data Lakehouse Architecture

The client’s existing on-premises big data infrastructure struggles with scalability, high maintenance costs, limited data governance, and inadequate data access control, impeding their ability to efficiently process, analyze, and deliver personalized content recommendations to a global user base. They require a seamless, secure migration to a modern cloud data platform that can handle massive datasets, support collaborative analytics, and reduce operational overhead without disrupting ongoing content delivery and user engagement.

A large-scale digital content provider offering access to millions of digital titles, including books, audiobooks, articles, and multimedia content through multi-platform distribution channels.

• Implement a cloud-based unified data platform incorporating data warehouses and data lakes to support analytics and AI workloads in a consolidated environment.
• Ensure scalable architecture that supports high-volume data processing, historical data management, and real-time streaming data handling.
• Reduce platform maintenance and operational costs through adoption of elastic cloud infrastructure and decoupled compute-storage architecture.
• Enhance data security, governance, and compliance to meet audit and regulatory requirements.
• Facilitate easy access for Data Scientists and Data Analysts via a unified user interface and automated pipeline management tools.
• Migrate existing big data systems from legacy technologies to modern, cost-effective cloud solutions with minimal business disruption.

• Unified analytics and AI workload management on a single platform.
• Implementation of a data lakehouse architecture to store, process, and analyze large-scale structured and unstructured data.
• Automated orchestration and monitoring of data pipelines using workflow management tools.
• Real-time data ingestion and processing capabilities.
• Robust data governance, auditing, and access control mechanisms.
• Provision of a user-friendly interface for Data Scientists and Analysts to manage workloads and access data efficiently.
• Decouple compute resources from storage to enable independent scaling and optimize costs.

• Existing data sources and data ingestion pipelines
• Security and identity management systems
• Audit and compliance systems
• Business intelligence and analytics tools
• Internal documentation and metadata repositories

• Support scalable data processing for datasets exceeding petabyte scale
• Ensure high availability and reliability of data pipelines
• Maintain strict data security and access controls conforming to industry standards
• Achieve near real-time data processing latency for streaming data
• Optimize for cost-efficiency with elastic resource scaling

The migration to a cloud-based, unified data platform is projected to significantly reduce operational costs—potentially saving thousands of dollars monthly—while enhancing data processing performance, scalability, and security. This transformation will empower data teams to deliver faster, more reliable insights and personalized content recommendations, ultimately improving user engagement and supporting global content distribution strategies.