Amine Chafai
Cloud Solution Architect at Microsoft
Cloud Solution Architect, Microsoft
Cloud Solution Architect, Microsoft
مهندس حلول سحابية في شركة مايكروسوفت
Redmond, Washington, United States
Actions
Amine Chafai is a Cloud Solution Architect at Microsoft, specializing in data engineering, analytics, and real-time intelligence solutions on the Microsoft data platform. He works closely with enterprise customers and partners to design and implement scalable, end-to-end analytics architectures, with a strong focus on Microsoft Fabric, advanced analytics, and AI-powered data solutions.
With a strong background in big data and analytics, Amine has supported organizations across multiple industries in modernizing their data estates, improving operational efficiency, and enabling data-driven decision-making through robust, production-ready architectures. His experience spans real-time analytics, data ingestion pipelines, and analytics solution optimization using Microsoft technologies.
Amine is an experienced conference speaker and has previously delivered sessions at FabCon Stockholm and FabCon Vienna, where he shared practical insights and real-world implementations of Microsoft Fabric and analytics solutions with the community.
He holds a Master’s degree in Computer Science from INSEA (Rabat, Morocco) and several Microsoft technical certifications. Amine is passionate about knowledge sharing, continuous learning, and helping organizations unlock the full value of their data through modern analytics platforms.
Amine Chafai est Cloud Solution Architect chez Microsoft, spécialisé dans l’ingénierie des données, l’analytique et les solutions de renseignement en temps réel sur la plateforme de données Microsoft. Il travaille en étroite collaboration avec des clients et partenaires grands comptes afin de concevoir et mettre en œuvre des architectures analytiques évolutives de bout en bout, avec un fort accent sur Microsoft Fabric, l’analytique avancée et les solutions de données pilotées par l’IA.
Fort d’une solide expertise en big data et en analytique, Amine a accompagné des organisations de multiples secteurs dans la modernisation de leurs environnements de données, l’amélioration de l’efficacité opérationnelle et la mise en place d’une prise de décision basée sur les données, grâce à des architectures robustes et prêtes pour la production. Son expérience couvre l’analytique en temps réel, les pipelines d’ingestion de données et l’optimisation de solutions analytiques basées sur les technologies Microsoft.
Amine est un conférencier expérimenté et a déjà animé des sessions lors de FabCon Stockholm et FabCon Vienna, où il a partagé avec la communauté des retours d’expérience concrets et des implémentations réelles autour de Microsoft Fabric et des solutions analytiques.
Amine a un diplome d'Ingénieur d'Etat en informatique de l’INSEA (Rabat, Maroc) et de plusieurs certifications techniques Microsoft. Amine est passionné par le partage de connaissances, l’apprentissage continu et l’accompagnement des organisations pour exploiter pleinement la valeur de leurs données grâce à des plateformes analytiques modernes.
Amine Chafai es Cloud Solution Architect en Microsoft, especializado en ingeniería de datos, analítica y soluciones de inteligencia en tiempo real sobre la plataforma de datos de Microsoft. Trabaja estrechamente con clientes empresariales y socios para diseñar e implementar arquitecturas analíticas escalables de extremo a extremo, con un fuerte enfoque en Microsoft Fabric, la analítica avanzada y las soluciones de datos impulsadas por IA.
Con una sólida trayectoria en big data y analítica, Amine ha apoyado a organizaciones de múltiples sectores en la modernización de sus entornos de datos, la mejora de la eficiencia operativa y la habilitación de una toma de decisiones basada en datos mediante arquitecturas robustas y listas para producción. Su experiencia abarca analítica en tiempo real, canalizaciones de ingestión de datos y la optimización de soluciones analíticas utilizando tecnologías de Microsoft.
Amine es un ponente con experiencia y ha impartido sesiones anteriormente en FabCon Stockholm y FabCon Vienna, donde compartió con la comunidad conocimientos prácticos e implementaciones reales de Microsoft Fabric y soluciones analíticas.
Es Máster en Ciencias de la Computación por el INSEA (Rabat, Marruecos) y cuenta con varias certificaciones técnicas de Microsoft. A Amine le apasiona el intercambio de conocimiento, el aprendizaje continuo y ayudar a las organizaciones a desbloquear todo el valor de sus datos mediante plataformas analíticas modernas.
أمين الشافعي هو مهندس حلول سحابية في شركة مايكروسوفت، متخصص في هندسة البيانات، والتحليلات، وحلول الذكاء اللحظي على منصة بيانات مايكروسوفت. يعمل عن كثب مع العملاء من المؤسسات والشركاء لتصميم وتنفيذ معماريات تحليلية شاملة وقابلة للتوسع، مع تركيز قوي على Microsoft Fabric، والتحليلات المتقدمة، وحلول البيانات المدعومة بالذكاء الاصطناعي.
يمتلك أمين خبرة قوية في مجالي البيانات الضخمة والتحليلات، حيث دعم منظمات من مختلف القطاعات في تحديث بيئات البيانات الخاصة بها، وتحسين الكفاءة التشغيلية، وتمكين اتخاذ قرارات قائمة على البيانات من خلال معماريات قوية وجاهزة للإنتاج. وتشمل خبرته التحليلات في الوقت الحقيقي، وخطوط استيعاب البيانات، وتحسين حلول التحليلات باستخدام تقنيات مايكروسوفت.
أمين متحدث تقني متمرس، وقد قدم سابقًا جلسات في FabCon Stockholm و FabCon Vienna، حيث شارك المجتمع خبراته العملية وتطبيقات واقعية باستخدام Microsoft Fabric وحلول التحليلات.
يحمل أمين شهادة مهندس دولة في علوم الحاسوب من المعهد الوطني للإحصاء والاقتصاد التطبيقي (INSEA) في الرباط، المغرب، إضافة إلى عدة شهادات تقنية من مايكروسوفت. وهو شغوف بمشاركة المعرفة، والتعلم المستمر، ومساعدة المؤسسات على تحقيق أقصى قيمة من بياناتها من خلال منصات تحليلات حديثة.
Badges
![Microsoft AI Navigator [Azure]](https://sessionize.com/image/89d9-400o400o3-H13Hn1k4aLvnjM22AwWarx.png "Microsoft AI Navigator [Azure]")
![Microsoft AI Navigator [Fabric]](https://sessionize.com/image/348e-400o400o3-7mFjUVXWUJXvosyCWfM8B.png "Microsoft AI Navigator [Fabric]")
Area of Expertise
Topics
Building production-ready Fabric Data Agents
In this session, we cut through the hype and walk you through the architecture, design patterns, and governance guardrails needed to put Data Agents into production. You'll leave with actionable techniques for grounding agents on governed semantic models, enforcing security boundaries, and keeping answers accurate and explainable at scale.
Many organizations are moving beyond proof-of-concept and asking the harder questions: how do we trust the answers? how do we control costs? how do we govern who sees what?
We demystify in this session how Fabric Data Agents process user questions, determine the most relevant data source: whether a Lakehouse, Warehouse, Power BI semantic model, KQL database, or Microsoft Graph and invoke the appropriate tool to generate, validate, and execute SQL, DAX, or KQL queries.
We will cover five concrete production patterns:
1. Semantic model quality as the deciding factor.
2. Security that travels with the data.
3. Multi-agent architecture patterns.
4. Observability and cost controls.
5. Rollout strategy.
By the end of this session, you will be able to:
- Explain how Fabric Data Agents resolve natural language questions into SQL, DAX, or KQL queries across Lakehouses, Warehouses, semantic models, and KQL databases and where that process breaks down in production.
- Assess the readiness of your semantic models for agent grounding, and apply a "prep for AI" checklist to close the most common gaps before deployment.
- Design a least-privilege security architecture for Data Agents using Entra ID credentials, Purview DLP policies, and OneLake row- and column-level security.
- Compose a multi-agent solution that connects a Data Agent as the conversational analytics layer with operations agents for real-time monitoring and automated response.
- Define a phased rollout strategy: from single-domain pilot to org-wide deployment, with the governance controls, cost guardrails, and observability mechanisms needed to sustain trust at scale.
Real-Time RAG in Microsoft Fabric: Streaming Context for Intelligent Decisions
This session explores how Microsoft Fabric's Real-Time Intelligence can feed live signals into a RAG architecture, enabling AI agents to detect, reason, and respond with up-to-the-second insights. We'll demonstrate how combining RTI with RAG transforms dashboards into actionable copilots.
Monetizing Data with Fabric: Turning Analytics into Revenue Streams
Discover how organizations can monetize data using Microsoft Fabric by creating new revenue streams, optimizing pricing, and enabling data-as-a-service models, all while ensuring governance and scalability.
From Sensor to Insight: Building a Real-Time IoT Pipeline with Raspberry and Fabric
We turned a handful of sensors and a Raspberry Pi into a fully operational Fabric Real-Time Intelligence pipeline, and in this session, we show you exactly how.
We'll walk through a live demo of an IoT pipeline built from scratch: connecting light, temperature, and humidity sensors directly into Fabric RTI. You'll see the full data journey: sensor wiring, edge data capture, event streaming, Real-Time Dashboards and Activators. Learn about our implementation journey, hard-won best practices, and a blueprint you can replicate tomorrow.
This is a live demo instead of slides-only session. We will physically alter the environment in real-time: changing light colors and humidity levels in front of the audience and watch the entire pipeline react end-to-end. From the moment a sensor detects a change, you'll see the data propagate through the Raspberry Pi edge layer, flow into Fabric Eventstream, land in the KQL Database, update the Real-Time Dashboard, and trigger Activator alerts — all within seconds.
In this session we will cover:
- Capture environmental readings by connecting physical sensors of light (LDR/RGB), temperature and humidity to a Raspberry Pi interface.
- Edge data collection through a lightweight Python service running on Raspberry Pi, responsible for polling sensors, normalizing readings, and managing local buffering during connectivity gaps.
- Ingest event streaming into Fabric RTI using Eventstream with schema enforcement and timestamp alignment handled at the edge.
- Persist data into KQL Database where it can queried and visualized in Real-Time Dashboards to enabling live monitoring.
- Trigger automated alerts and actions using Activator when the defined conditions are met. Which closes the loop from raw data to intelligent response.
Key Takeaways :
- A clear architectural blueprint for Raspberry Pi to Fabric RTI pipelines.
- Practical guidance on hardware implementation, data flow from sensors to Fabric RTI, and Eventstream configuration.
- A realistic picture of the trade-offs between cost, latency, and reliability in IoT + Fabric scenarios.
- Code snippets and a GitHub reference repository shared post-session.
From Insight to Action: Building AI Agent Systems with Real-Time Intelligence in Microsoft Fabric
As real-time data becomes mission-critical, the next frontier in analytics is not just insight, but intelligent action! In this session, we will walk through a cutting-edge architecture that combines Microsoft Fabric's Real-Time Intelligence capabilities with AI Agent systems and automation tools to create fully autonomous, decision-driven workflows.
You'll discover how streaming data is ingested through Fabric's Event Streams and analyzed using KQL and notebooks to detect anomalies or thresholds. From there, alerts trigger a GPT-powered AI Agent using Azure OpenAI, which interprets context, queries additional insights if needed, and dynamically determines next best actions. These actions are then executed via Power Automate or Logic Apps, whether it's notifying stakeholders, placing orders, or remediating an issue.
Join us for a technical deep dive into this end-to-end solution, see a live industry use case in action, and leave with an architecture blueprint to bring real-time, AI-driven automation to your organization.
Detect, Decide, Defend: Real-Time Fraud Detection at Scale with Microsoft Fabric
Fraud detection is evolving from reactive to proactive. In this session, we will demonstrate how Microsoft Fabric enables the implementation of Real-Time fraud detection solutions that leverage its powerful capabilities in streaming analytics, AI integration, and automated decision-making.
We will walk through the architecture of a fraud detection system that uses Microsoft Fabric Event Streams to ingest high-volume transaction data and apply KQL-based Real-Time analytics to identify suspicious patterns and anomalies. With Azure OpenAI and Copilot, we will show how AI models can be utilized to enhance fraud detection accuracy by scoring transactions and identifying complex fraud patterns.
Additionally, we will demonstrate how to trigger automated actions such as flagging transactions, alerting security teams, or blocking high-risk activities using Power Automate and Logic Apps. By the end of this session, you will understand how to design and deploy a scalable, end-to-end fraud detection pipeline on Microsoft Fabric, providing Real-Time insights while minimizing false positives and improving overall detection accuracy.
Unlocking Real-Time Analytics with Microsoft Fabric: From Data Ingestion to Insights
Dive into the world of real-time analytics with Microsoft Fabric in this comprehensive session. Learn how to seamlessly ingest, process, and analyze streaming data to derive actionable insights instantly.
This session will cover:
- Setting up real-time data ingestion pipelines
- Utilizing Microsoft Fabric's built-in capabilities for data processing and analytics
- Implementing machine learning models for real-time predictions or anomaly detection
- Best practices for optimizing performance and ensuring data integrity
Automating Invoice Processing with Microsoft Fabric: A Step-by-Step Implementation Guide
This session will walk you through the detailed process of automating invoice processing using Microsoft Fabric and Azure AI. From data ingestion and preprocessing to applying machine learning models for extraction and validation, this comprehensive guide will demonstrate how to streamline invoice management efficiently.
Key Points:
- Setting up data ingestion pipelines for invoice data
- Preprocessing and structuring invoice data
- Using a Machine Learning model for invoice data extraction
- Automating validation and error handling
- Integrating with Power BI for real-time invoice monitoring and reporting
- Best practices and optimization tips for robust invoice processing
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top