Naveen Ayalla
Senior Data Engineer at Microsoft | Data Engineering | Microsoft Fabric | Azure | AI & Enterprise Analytics
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Naveen Ayalla is a Senior Data Engineer at Microsoft with over 10 years of experience designing and building enterprise-scale data platforms, cloud analytics solutions, and modern data architectures. His work focuses on Microsoft Fabric, Azure data services, distributed data processing, and AI-powered analytics.
Naveen is passionate about helping organizations build scalable, secure, and high-performing data platforms. He has authored technical articles on data engineering and AI, contributed to the developer community through open-source projects, and served as a judge for the 2026 Stevie Awards for Technology Excellence.
In his sessions, Naveen shares practical architecture patterns, real-world implementation strategies, and performance optimization techniques that data engineers and architects can apply in enterprise environments.
Microsoft Fabric Performance Tuning: Lessons from Enterprise Workloads
Spark optimization
Delta tables
Capacity utilization
Query performance
Data Warehouse optimization
Monitoring
Cost reduction
This is a topic many teams struggle with and tends to generate good discussion.
From Zero to Production: Building an End-to-End Microsoft Fabric Data Platform
Learn how to design and deploy a production-ready Microsoft Fabric solution using OneLake, Data Factory, Lakehouse, Warehouse, Notebooks, Semantic Models, and Power BI. This session covers architecture, CI/CD, governance, security, monitoring, and cost optimization with a live demo.
From Prototype to Production: Deploying Enterprise Generative AI on Azure
Many organizations successfully build AI prototypes but struggle to deploy reliable enterprise solutions. Production systems require secure architecture, governance, observability, cost management, and operational excellence.
This session explores the complete lifecycle of deploying enterprise Generative AI applications on Azure. We'll discuss Azure AI services, Azure OpenAI, monitoring, identity, security, responsible AI, CI/CD, and production architecture patterns.
Attendees will leave with practical guidance for designing scalable, maintainable, and production-ready AI solutions.
Databricks for Data Engineers: From Ingestion to AI
Modern data platforms are evolving beyond traditional ETL pipelines. With Databricks and AI, data engineers can automate repetitive tasks, improve data quality, optimize performance, and build scalable analytics platforms faster than ever before.
In this session, I'll demonstrate practical techniques for designing enterprise-grade data platforms using Databricks. We'll cover Delta Lake, Apache Spark optimization, medallion architecture, data quality, pipeline automation, AI-assisted development, and cost optimization strategies that help teams build reliable, high-performing solutions.
Whether you're modernizing an existing platform or building one from scratch, you'll leave with practical patterns, real-world lessons, and techniques you can immediately apply to your own data engineering projects.
End-to-End Data Engineering with Microsoft Fabric: From Ingestion to Insights
Cover:
Data Factory
Lakehouse
Spark
Warehouse
Power BI
Real-Time Intelligence
Monitoring
Performance
Data Engineering Best Practices in Microsoft Fabric: Lessons Learned from Enterprise Implementations
nclude:
Workspace organization
CI/CD
Deployment pipelines
Git integration
Naming conventions
Monitoring
Governance
Security
Building Production-Ready Enterprise AI Agents with Azure AI and Model Context Protocol (MCP)
Large Language Models become significantly more valuable when they can securely interact with enterprise systems. The Model Context Protocol (MCP) has emerged as a standard for connecting AI agents with business applications, APIs, and enterprise data.
In this session, we'll build a production-ready MCP server on Azure and explore the architecture required for secure, scalable enterprise AI solutions. We'll cover authentication, authorization, observability, tool registration, deployment, and best practices for integrating Azure OpenAI with enterprise services.
Through live demonstrations and real-world architecture patterns, attendees will learn how to design AI agents that move beyond simple chat experiences into secure enterprise automation.
Building Production-Ready Enterprise AI Agents with Azure AI and Model Context Protocol (MCP)
Large Language Models become significantly more valuable when they can securely interact with enterprise systems. The Model Context Protocol (MCP) is rapidly emerging as a standard for connecting AI agents to enterprise applications, APIs, and data sources while maintaining security and governance.
In this session, we'll explore how to build production-ready enterprise AI agents using Azure AI, Azure OpenAI, and MCP. We'll discuss architecture patterns, authentication, authorization, tool registration, observability, deployment, and operational best practices for enterprise environments.
Through live demonstrations and practical implementation examples, attendees will learn how to move beyond simple chatbots and build AI agents capable of securely interacting with business systems at scale. We'll also discuss common implementation pitfalls, governance considerations, and strategies for monitoring and maintaining enterprise AI workloads in production.
Building Enterprise Data Platforms with Microsoft Fabric: Architecture, Performance, and Best Practi
Microsoft Fabric simplifies modern analytics by bringing data engineering, data warehousing, real-time analytics, and business intelligence into a unified platform. Designing a production-ready Fabric environment, however, requires careful architectural decisions around scalability, governance, security, and cost optimization.
In this session, we'll explore practical patterns for building enterprise-scale data platforms with Microsoft Fabric. Using real-world implementation examples, we'll discuss Medallion architecture, Lakehouse design, Data Factory pipelines, Spark optimization, semantic models, security, monitoring, and capacity management.
Attendees will gain actionable guidance for designing scalable, secure, and high-performing Fabric solutions that support analytics and AI workloads.
Building Intelligent Enterprise Data Platforms with Microsoft Fabric and Azure AI
Modern data platforms are evolving beyond analytics into intelligent systems powered by AI. Microsoft Fabric and Azure AI enable organizations to build unified platforms supporting advanced analytics, semantic search, Retrieval-Augmented Generation (RAG), and AI copilots.
This session demonstrates practical architecture patterns for integrating Microsoft Fabric with Azure AI services to build scalable, AI-ready enterprise data platforms. We'll cover Lakehouse design, semantic models, governance, security, AI integration, performance optimization, and operational best practices.
Using real-world enterprise scenarios, attendees will gain practical guidance for designing modern data platforms that support both analytics and AI workloads while maintaining scalability, security, and cost efficiency.
Building Enterprise Data Platforms with Microsoft Fabric: Architecture, Performance
Session Abstract
Modern enterprises are rapidly adopting Microsoft Fabric to unify data engineering, analytics, data science, and business intelligence into a single SaaS platform. However, designing an enterprise-scale Fabric implementation requires more than simply migrating workloads—it demands a well-architected approach to scalability, governance, security, and performance.
In this session, attendees will learn how to build production-ready enterprise data platforms using Microsoft Fabric. We will explore proven architectural patterns, OneLake design, medallion architecture, data ingestion strategies, Lakehouse and Warehouse integration, Real-Time Intelligence, Data Factory pipelines, and semantic modeling with Power BI. The session will also cover performance optimization techniques, cost management strategies, governance with Microsoft Purview, and security best practices for large-scale deployments.
Drawing from practical implementation patterns and real-world engineering experience, this session provides actionable guidance for designing scalable, resilient, and high-performing data platforms that support analytics, AI, and enterprise reporting workloads.
Building AI-Ready Data Platforms with Microsoft Fabric and Azure AI
Topics:
Fabric Lakehouse
Azure AI
Azure OpenAI
AI-ready architecture
Vector search
RAG
Semantic models
Governance
Building Enterprise Data Platforms with Microsoft Fabric: Architecture, Performance, and Best Practi
Microsoft Fabric is transforming how organizations build modern analytics platforms, but designing an enterprise-ready architecture requires more than simply moving workloads to the cloud.
In this session, I'll share practical architectural patterns and lessons learned for building scalable, secure, and high-performance data platforms using Microsoft Fabric. We'll explore how to design end-to-end data pipelines, optimize Lakehouse performance, implement governance, and support analytics and AI workloads in enterprise environments.
Attendees will learn how to:
Design scalable data architectures using Microsoft Fabric.
Build efficient data ingestion and transformation pipelines.
Optimize Lakehouse and Warehouse performance.
Implement governance, security, and data quality best practices.
Integrate Fabric with Azure services for enterprise analytics.
Avoid common implementation pitfalls and performance bottlenecks.
This session is intended for data engineers, architects, and technical leaders looking for practical guidance beyond introductory Fabric demonstrations.
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