Microsoft Fabric and Azure consulting for data and AI engineering. We connect business applications, build governed lakehouses and deliver analytics and AI applications with explicit access, quality and release controls.
DAQ brings Fabric data engineering and Azure AI application engineering into a clear delivery plan. Fabric handles the analytical foundation; Azure services support the application requirements. We define how data, identities and operating responsibilities cross that boundary.
Data Factory pipelines coordinate ingestion and notebook processing. Bronze preserves source data, Silver conforms and validates it, and Gold publishes datasets for consumption. REST and database integrations follow source-specific authentication, increment and deletion rules.
In the deliveryAn ingestion framework, notebook transformations and data contracts.
Gold tables feed semantic models with shared measures and defined access rules. Direct Lake reads Delta data in OneLake; SQL analytics endpoints support SQL consumers and supported connectors. Each path has its own permissions and freshness behavior.
In the deliveryA semantic model, consumption interfaces and reconciliation checks.
Retrieval and agent workflows using Azure AI Search and Microsoft Foundry where they fit the requirements. We design document permissions, tool authorization, human approval and evaluation around the application. A signed-in user alone does not establish document-level access.
In the deliveryA retrieval design, evaluation set and tested action boundaries.
Microsoft Entra identities, Fabric permissions and Azure role assignments matched to the workload. We use workload identities where supported and explicitly secure remaining service connections. Deployment, execution and end-user access are reviewed separately.
In the deliveryAn identity map, least-privilege access and documented connection owners.
Configuration, notebooks and supported platform artifacts are versioned and released through a tested deployment process. Run records, data quality results and alerts make failures inspectable. Retry, retention and maintenance policies are defined for the actual source and workload.
In the deliveryA deployment repository, run evidence and a practical handover.
Five REST APIs and one SQL Server source integrated into a configuration-driven Fabric platform. Bronze, Silver and Gold prepare data now used by Salesforce Data Cloud and Power BI reporting.
A shared ingestion framework replaces individual source packages, with source-specific schedules and one governed reporting model.
No. A Fabric data platform can serve reporting and downstream applications independently. Azure AI services are added when the use case needs retrieval, model access or agent execution. The design follows the workload and its security requirements.
Yes, through separately configured consumption paths. In the healthcare project, prepared Gold data feeds Salesforce Data Cloud through its Fabric connector and Power BI through Direct Lake. Cross-source identity resolution stays in Salesforce; reporting reads the governed analytical model.
Read the six-source Fabric projectYes. We assess the existing pipelines, SQL workloads, reports and identity setup before defining the target. Migration and reconciliation can be scoped alongside the platform work, with a cutover plan for the workloads being replaced.
Explore migration to Microsoft FabricStart with a data or AI workload that needs a dependable foundation. We can scope the integration, platform implementation or application, including the boundaries between Fabric, Azure and your existing systems.
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