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Power BI Optimization and Cloud Migration Enhancement for Customer Experience Analytics Platform
  1. case
  2. Power BI Optimization and Cloud Migration Enhancement for Customer Experience Analytics Platform

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Power BI Optimization and Cloud Migration Enhancement for Customer Experience Analytics Platform

itransition.com
Business services
Information technology
Gaming

Challenges in Power BI Performance and Cost Efficiency Post-Migration

After migrating to Azure Data Lake and Databricks, the client faced critical issues with Power BI including excessive data model size requiring higher P3/P4 subscriptions, system timeouts/crashes during data loading, incremental refresh times exceeding SLA requirements (3-6 hours vs 1-hour target), and inefficient data architecture causing performance bottlenecks.

About the Client

Global customer experience (CX) company providing tech solutions and data insights for gaming and entertainment industries with operations in 95 countries

Optimization Goals for Power BI and Cloud Infrastructure

  • Reduce Power BI data model size to fit within P1 subscription limits
  • Improve incremental data refresh performance to meet 1-hour SLA
  • Eliminate redundant data storage and processing overhead
  • Optimize cloud architecture for scalable client onboarding

Core System Optimization Requirements

  • Data model compression through column pruning and encoding optimization
  • Incremental refresh configuration with time-based partitioning
  • Implementation of DirectQuery for large tag tables
  • Date dimension optimization for time zone handling
  • Cardinality reduction in numeric columns

Cloud and Analytics Technologies

Microsoft Power BI
Azure Data Lake Storage
Azure Databricks
Delta Lake architecture
Azure Data Factory

System Integrations

  • Power BI to Azure Databricks connection
  • Azure Data Factory pipeline orchestration
  • Azure DevOps for documentation and CI/CD

Performance and Scalability Requirements

  • Support 25% annual data growth without performance degradation
  • Ensure 99.9% system reliability during peak loads
  • Maintain enterprise-grade data security in cloud environment
  • Enable seamless scalability for new client onboarding

Expected Business Impact of Power BI Optimization

Projected 40-60% reduction in Power BI infrastructure costs through P1 subscription retention, 70% faster data refresh cycles meeting SLA requirements, 30% improvement in report generation performance, and scalable architecture supporting 200+ monthly users with 25% annual data growth. Enhanced system stability will prevent revenue loss from client service disruptions.

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