Workflows of Highly Functional App & Data Engineering Teams

How does a highly functional development team manage enterprise data engineering? They focus on the core principles of Schema Management, Standards Enforcement, Database Testing, and Automated Incremental Deployment. Microsoft brings an agonizing menu of choices to architects and engineers; they can be a challenge to wade through. This session can help. Let’s review how some of the world’s largest and most critical app & data engineering teams handle change management, observability, and quality at enterprise scale.

Goals: • Understand how enterprise teams manage schema control, including versioning, migrations, and updates, to ensure data consistency across all environments. • Explore how enterprise teams ensure engineering quality through automated testing, code reviews, and standards enforcement. • Learn how enterprise teams automate reliable delivery through continuous integration and deployment, reducing errors and improving efficiency.

Introducing the SQL MCP Server

Modern apps use language models, but how do those models safely interoperate with your company’s backend data without putting the business at risk? SQL MCP Server answers that question. It is Microsoft’s open source MCP server for secure access to SQL Server, Azure SQL, PostgreSQL, Cosmos DB, and MySQL, running in the cloud or on premises at enterprise scale. It provides a feature-rich database API designed to help models understand and work with your data while enforcing permissions, policies, and operational boundaries.

Built on the same high-scale engine that powers Microsoft Fabric’s own GraphQL API, SQL MCP Server is designed for both scale and developer productivity. With a rich inner loop and a cross-platform command line, it works just as well for enterprise workloads as it does for engineers innovating on the ground.

SQL ❤️ AI Agents

SQL is becoming a powerful foundation for agentic applications. In this session, we will look at how to prepare relational data for semantic search using external model embeddings, text chunking, and DiskANN through SQL Server’s vector index capabilities. Then we will show how SQL MCP Server gives agents, including agents built in Microsoft Foundry, governed access to that data through security boundaries, permissions, and practical guardrails.

You will leave with a practical understanding of how SQL Server can support modern AI application patterns while keeping enterprise data close, queryable, protected, and safe for agentic workloads.

1. Understand how to prepare relational data for semantic search using external model embeddings, text chunking, and SQL Server vector indexes.
2. Learn how SQL MCP Server enables agents, including agents in Microsoft Foundry, to access SQL data through governed and secure interfaces.
3. Explore practical guardrails for agentic workloads, including permissions, query boundaries, data protection, and database compute safety.

SQL ❤️ NoSQL Databases

Think SQL Server is just rows and columns? Think again. SQL has been quietly adding capabilities that might surprise even experienced developers. Native binary JSON, JSON indexes, computed columns, triggers, and more. Turns out, you don't need to abandon relational databases to get the flexibility of NoSQL.

In this session, we'll explore how to build document-centric applications with SQL Server. We'll query JSON, index it, handle large documents, react to changes, and combine flexible documents with relational data. We'll even go beyond JSON into XML, regular expressions, and fuzzy search. It's a lot more than you might expect from a relational database, and some of these features are pretty amazing.

You'll learn

How SQL Server supports NoSQL-style applications with native binary JSON, JSON functions, and JSON indexes.

How to build practical document patterns using computed columns, triggers, large documents, and hybrid relational designs.

How XML, regular expressions, fuzzy search, and other SQL capabilities extend what's possible without adding another database engine.

SQL ❤️ Software Developers

SQL Server continues to invest in the application developer experience. In this session, we will look at enhancements across drivers, Entity Framework, modern SQL data types, Aspire integrations, Linux SQL containers, database projects, schema source control, CI/CD workflows, and Community Toolkit features for Data API builder. We will also explore how developers can access SQL securely through REST, GraphQL, and SQL MCP Server, while using the VS Code MSSQL extension to streamline the inner developer loop.

You will leave with a practical view of how SQL Server helps developers build modern applications faster, more securely, and with fewer gaps between local development, APIs, agents, schema changes, and production data.

1. Understand recent SQL Server developer investments across drivers, Entity Framework, modern data types, Aspire, and Linux containers.
2. Learn how database projects, schema source control, and CI/CD integrations help teams manage database changes with the same discipline as application code.
3. Explore how SQL MCP Server and the VS Code MSSQL extension improve agent access, local development, and the developer loop.

SQL ❤️ Multimodel Schemas

SQL Server continues to expand beyond a single way of modeling data. In this session, we will look at multimodel schema design across relational tables, documents, graph relationships, vectors, spatial data, and time-based data patterns. We will explore how these models can work together in one database, allowing developers to choose the right shape for each part of the application without splitting data across disconnected systems.

You will leave with a practical view of how SQL Server supports flexible schema design while preserving queryability, governance, indexing, security, and operational consistency.

1. Understand how SQL Server supports multimodel schemas across relational, document, graph, vector, spatial, and temporal patterns.
2. Learn when to use each model and how to combine them within a single application architecture.
3. Explore how indexing, query patterns, security, and governance apply across different data shapes in SQL Server.

What Every Developer Should Know About Relational Databases

Relational databases are the foundation of many of the applications developers build every day, but they are much more than tables and rows. In this session, we will introduce the core concepts behind relational database systems, including tables, columns, relationships, views, indexes, computed values, procedures, parameters, and functions. We will also look at more advanced capabilities such as materialized views, temporal tables, memory-optimized tables, performance tuning, scale, cost, and built-in database features that help applications grow.

You will leave with a practical understanding of what relational databases are, how they work, and why they remain one of the most important tools for building reliable, secure, and scalable applications.

1. Understand the core building blocks of relational databases, including tables, columns, relationships, views, indexes, procedures, and functions.
2. Learn how relational database features support performance, scale, security, cost control, and application reliability.
3. Explore modern database developer capabilities, including tools, drivers, libraries, endpoints, engines, and recent innovations that make relational databases easier to use in modern applications.

A Developer's Guide to Database Patterns

Is your database a pain in the neck? Are your queries slower than they should be? Are you sure you're using the right approach? You're not alone. Database development is full of patterns, techniques, and best practices that most developers never learn. And sometimes, what seems like a good idea turns out to be an antipattern that costs you performance, scalability, or money.

In this session, we'll explore practical database patterns I've collected from some of Microsoft's largest and most demanding customers. We'll talk about indexes, query design, data modeling, transactions, concurrency, and the little decisions that make a big difference. We'll also look at common mistakes, approaches that don't scale, and techniques that can dramatically simplify your application. Some might surprise you. Others will make you wonder why you aren't already using them.

You'll learn

How to choose the right database feature, write efficient queries, and recognize common performance antipatterns before they become problems.

How to apply practical database design patterns for data modeling, transactions, concurrency, and reliable data access.

How to recognize costly database antipatterns and apply proven techniques to improve performance, scalability, maintainability, and cost.