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World Congress 2025 • Aug 20, 2025 • Session details

Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents

Dr. Alexander Wachtel , Hanna Schwab

Eliminate fragmented data pipelines by centralizing your architecture with Microsoft Fabric. See how conversational AI data agents extract operational insights directly from OneLake without complex SQL.

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#1 about 2 min

Introduction to Microsoft Fabric and data agents

Setting the stage for exploring Microsoft Fabric capabilities by outlining session goals and core data concepts.

#2 about 2 min

Traditional data architecture before Microsoft Fabric

How disjointed legacy environments necessitated the patching of multiple disparate services for data ingestion and visualization.

#3 about 1 min

Unifying services with the Microsoft Fabric platform

Consolidating disparate pipelines into a single platform accelerates deployment and simplifies unified user access management.

#4 about 2 min

Centralizing disparate data storage with Microsoft OneLake

Adopting a unified data lake approach centralizes scattered file systems and database schemas without painful custom integrations.

#5 about 3 min

Introducing conversational queries with AI data agents

Navigating unstructured database syntax hurdles by utilizing natural language interfaces mapped directly against complex internal storage.

#6 about 2 min

Pharmaceutical inventory and demand forecasting use case

Managing complex buying criteria effectively through continuous visibility into shifting inventory allocations and pending wholesale demands.

#7 about 1 min

Integrating enterprise business applications for unified analytics

Overcoming operational silos by streaming diverse business applications continuously into a universally accessible analytics architecture.

#8 about 1 min

Syncing transactional updates through live database mirroring

Mirroring SQL databases eliminates risky batch export delays by cascading external pricing adjustments into the ecosystem instantly.

#9 about 1 min

Building modern data pipelines for legacy exports

Translating bulky legacy customer system exports seamlessly into high-performance Delta tables for scalable analytic pipelines.

#10 about 2 min

Tracking dynamic inventory levels with real-time intelligence

Eliminating asynchronous storage blind spots to guarantee machine learning algorithms consume accurate, real-time localized inventory counts.

#11 about 2 min

Forecasting product demand using Azure Machine Learning

Anticipating sudden purchasing trends natively using embedded machine learning notebooks that identify statistically probable stock depletions.

#12 about 2 min

Accelerating data exploration with conversational AI queries

Bypassing steep database querying learning curves by interacting intuitively with datasets using casual language questions.

#13 about 3 min

Configuring system prompts and schema metadata contexts

Correcting artificial intelligence hallucinations by explicitly declaring structural metadata mappings and specialized internal geographic abbreviations.

#14 about 2 min

Analyzing top products and granular production costs

Verifying system prompt stability by processing multipart questions regarding overlapping manufacturing costs and regional inventory fulfillment.

#15 about 1 min

Publishing custom data agents to Azure Foundry

Scaling custom analytical contexts out uniformly by binding local data agents into overarching Azure AI instances.

#16 about 3 min

Current preview limitations of AI data agents

Working around preliminary release limitations restricting agent behavior from performing advanced time-series analysis or broad schema scans.

#17 about 3 min

Best practices for building a proof of concept

Validating new cloud architectures decisively by focusing exclusively on isolated initial proof-of-concept deployments before broad scaling.

Matching moments

4:20 min

Connecting data and modern AI with data fabric architecture

Andreas Christian · LIVE

2:36 min

Defining data fabric for modern data integration

Andreas Christian · LIVE

40 sec

Introduction to building real-world AI agent solutions

Dennis Zielke Dennis Zielke +1 · WWC 2025

1:13 min

Managing AI development with Azure AI Foundry

Ricardo Ricardo · WWC 2025

3:44 min

Building durable AI agents with Microsoft agent framework

Stas Lebedenko Stas Lebedenko · WWC Europe 2026

4:21 min

Scaling operations using Azure AI Foundry tools

Maxim Salnikov Maxim Salnikov · WWC 2025

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