✅ Data warehousing in Microsoft Fabric - options, best practices
Outcome: Consolidate fragmented data sources into a governed, scalable platform for analytics and AI.
Value: Reduce infrastructure complexity, accelerate time-to-insight, and enable cross-domain analytics without data duplication.
(Ideal for customers modernizing legacy EDWs or moving from on-prem to cloud)
✅ Implementation and best practices for data ingestion
Outcome: Automate ingestion from multiple sources (ERP, CRM, IoT) with governance and cost efficiency.
Value: Lower operational overhead, improve data freshness, and support real-time decision-making.
(Relevant for organizations struggling with siloed data and manual ETL)
✅ Data consumption in Microsoft Fabric (Power BI, APIs, Data Agents etc)
Outcome: Democratize analytics with self-service BI and conversational insights.
Value: Empower business users, reduce dependency on IT, and accelerate decision cycles.
(Great for customers aiming for data-driven culture and AI-powered insights)
✅ Data Governance
Outcome: Ensure compliance with GDPR, HIPAA, and internal policies while enabling self-service analytics.
Value: Reduce regulatory risk, improve trust, and enable secure data sharing across teams.
(Critical for regulated industries like finance, healthcare, and public sector)
✅ Monitoring and audit
Outcome: Gain visibility into usage, optimize capacity, and detect anomalies proactively.
Value: Improve operational efficiency, prevent misuse, and support audit readiness.
(Ideal for enterprises scaling Fabric adoption across multiple domains)
✅ Fabric capacity management
Outcome: Scale analytics workloads predictably without cost overruns.
Value: Optimize TCO, ensure SLAs for mission-critical workloads, and enable sustainable growth.
(Relevant for organizations moving from P-SKU to F-SKU or consolidating BI and AI workloads)
✅ Q&A