Cloud Data Migration and Modernization for Cost Optimization and Accelerated Software Deployment

The client operates with on-premises SQL servers hosting extensive media assets and associated data, resulting in slow data processing, high maintenance costs, and limited scalability. Additionally, the existing legacy CI/CD pipeline hampers rapid software development and increases system downtime, impacting overall business agility.

A large-scale digital media content platform seeking to optimize data storage, processing, and analytics through cloud migration to enhance scalability, cost-efficiency, and development agility.

• Migrate data storage and processing to a scalable, cost-effective cloud-based data platform (e.g., cloud data warehouse and ETL tools).
• Reduce data processing time and simplify maintenance for large datasets.
• Enhance data analysis capabilities with an intuitive, feature-rich cloud-based business intelligence platform.
• Accelerate software deployment cycles and improve deployment quality by modernizing the CI/CD pipeline.
• Achieve improved scalability, maintainability, and cost-efficiency of data infrastructure.

• Migration of legacy SQL-based data storage to a cloud-based data platform such as a data build tool (e.g., dbt) and a cloud data warehouse (e.g., Snowflake).
• Conversion of existing reports from traditional reporting tools to an advanced cloud BI platform with customizable dashboards and visualization (e.g., Looker).
• Development of over 70 optimized, dynamic reports for financial, sales, and marketing departments, and customer-facing analytics.
• Migration of existing CI/CD pipelines from legacy tools to modern cloud-enabled solutions (e.g., GitHub Actions).
• Automation and orchestration of data workflows to support scalable, real-time analytics.

• Existing on-premises SQL servers for data extraction
• Business intelligence reporting tools for report migration
• Development tools and repositories for CI/CD pipeline integration
• Cloud data platform APIs for seamless data flow
• Monitoring and alerting systems for system health and security

• Scalability to handle increasing data volumes with minimal latency
• High availability and fault tolerance for data processing and reporting platforms
• Data security and compliance with relevant standards
• Performance targets: reduce data processing time and deployment cycle times by at least 30-50%
• Maintainability and ease of updates to support ongoing development

The project is expected to significantly improve data processing efficiency and scalability, reduce operational costs, enhance analytics capabilities, and accelerate software deployment cycles. These improvements will result in faster decision-making, better resource utilization, and a more agile development environment, mirroring a potential 30-50% reduction in data processing and deployment times, along with increased system reliability and user satisfaction.