The ITRex team assessed the client's existing infrastructure and
AI readiness before proposing anything. The diagnosis was clear: the IaaS architecture needed to go. We recommended rebuilding the data and analytics infrastructure on Microsoft Azure as a platform-as-a-service (PaaS)—an architecture that would remove the processing bottlenecks, scale with data volume, and open the door to self-service analytics and
integrated AI.
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Data storage modernization. ITRex replaced on-premises databases with Azure Data Lake Storage and Azure SQL Data Warehouse, handling scalable ingestion, storage, and high-performance querying across all ePOS sources.
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ETL pipeline overhaul. We used Azure Data Factory to manage ingestion pipelines and Azure Databricks to transform raw data into clean, standardized, and analytics-ready datasets.
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BI and reporting enhancement. Our team migrated the existing workloads to Azure Analysis Services, delivering fast, real-time insights through Power BI dashboards.
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Application modernization. ITRex ported the client's custom application for BI maintenance to Azure App Service and moved data storage to Azure SQL Database.
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AI-powered insights. We integrated Azure AI Services to layer predictive
machine learning models onto the platform—giving non-technical employees access to forecasts and recommendations without requiring data team involvement.
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Cloud platform: Microsoft Azure
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Data storage: Azure Data Lake Storage Gen2 (raw/semi-structured data), Azure SQL Database (application data)
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Data warehouse: Azure Synapse Analytics
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Data ingestion & orchestration: Azure Data Factory
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Data processing & transformation: Azure Databricks
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Analytics & semantic layer: Azure Analysis Services
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Data visualization & reporting: Power BI
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Application hosting: Azure App Service
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AI & predictive analytics: Azure AI Services
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Governance & monitoring: Azure Active Directory (access control), Azure Monitor (observability)
