Data Model Optimization and Performance Enhancement for Cloud-Based Business Intelligence System

Gaming

Media

Entertainment

Challenges in Scaling and Performance of Cloud-Based BI Data Infrastructure

The client faces significant challenges related to scaling their Power BI solutions due to increasing data volume and complexity. Their existing on-premises data warehouse is costly and difficult to maintain. Post-migration to cloud-based platforms, their data models are too large, causing system timeouts, slow data refreshes, and higher subscription costs, impacting SLA adherence and user productivity.

About the Client

A large, globally-operating customer experience company providing multilingual support analytics and insights for clients in gaming and entertainment sectors.

Goals for Enhancing Data Infrastructure and Reporting Performance

Optimize the data model to fit within existing Power BI licensing constraints, avoiding unnecessary upgrades
Reduce data refresh times to meet SLA requirements, targeting incremental refresh periods of 30-60 minutes
Improve overall system reliability and scalability to accommodate data growth (~25% annually)
Enhance data load efficiency and reduce the duration of data ingestion processes
Implement best practices for data partitioning, encoding, and model structuring to support future extensibility

Core Functional Capabilities for a Scalable and Efficient Cloud BI System

Implementation of a Delta Lake-based ELT data lake architecture with optimized partitioning (e.g., by date, subject) for efficient data processing
Data ingestion orchestration via cloud-native tools (e.g., Azure Data Factory or equivalent) for automated, scheduled data loading
Transformation and preparation of data using scalable data engineering platforms (e.g., Azure Databricks or similar), with performance-oriented data partitioning and indexing
Data model optimization in Power BI, including removal of unused columns, data compression via encoding hints, and reduction of high-cardinality columns
Enhanced incremental refresh capabilities with configurable time periods (e.g., 1-day windows) and optimized column rounding

Recommended Technologies and Architectural Strategies for Scalable BI

Cloud data lake architecture based on Delta Lake principles

Azure Data Factory or equivalent for data ingestion orchestration

Azure Databricks for scalable data transformation and processing

Power BI Premium capacity (e.g., P3/P4) for large datasets, with optimized data models

Essential System Integrations for Data Pipeline and Reporting

External heterogeneous data sources (helpdesk tickets, emails, chats, surveys) via cloud data ingestion pipelines
Azure Data Lake Storage for centralized data repository
Power BI service for report distribution and user access
Azure Databricks notebooks for data transformation and data quality monitoring

Performance, Scalability, and Security Requirements

Support data models up to 21GB or more without system slowdown or failure
Reduce data refresh times from several hours to under 1 hour, aiming for incremental refreshes within 30-60 minutes
Ensure system security through role-based access controls and data encryption
Design for high availability, maintainability, and future extensibility to accommodate annual data growth (~25%)

Projected Business Benefits from Data Model Optimization and System Improvements

The project aims to significantly reduce data refresh times, improve system reliability, and optimize costs associated with cloud BI licensing. Expected outcomes include meeting SLAs consistently, supporting a growing user base (>200 users), and enabling scalable analytics infrastructure that adapts to a 25% annual increase in data volume, ultimately enhancing customer insights and support quality.