Rayfin and Fabicator Demo

Explain what the Rayfin SDK exposes to VS Code and demonstrate how to use Fabricator to deploy an actual, working application, Data Api Builder, and Fabric SQL Database in my FabricDemo Workspace

Old School Meets New School: SQL and Python in Microsoft Fabric

SQL has powered data work for decades, and for good reason — it remains one of the most efficient tools for querying, joining, and aggregating structured data. But modern data engineering demands more than querying tables. Files, messy inputs, repeatable pipelines, complex business logic, and automated deliverables have become the daily reality for data professionals. So where does SQL end and Python begin?
This session uses a side-by-side format — pairing a seasoned SQL expert against a new Python data engineer — to explore practical use cases where each tool shines within the Microsoft Fabric ecosystem. From Fabric notebooks running PySpark and pandas to SQL analytics endpoints and Lakehouses, attendees will see both approaches applied to real problems on the same platform. Rather than declaring a winner, the session builds a practical decision framework: SQL for set-based operations on clean, structured data; Python for automation, flexibility, and finished deliverables.
Attendees will leave with a clear mental model for choosing the right tool inside Microsoft Fabric, code examples they can apply immediately, and a newfound appreciation for what SQL and Python accomplish together that neither can do alone

Modern T-SQL Development & SQL Server 2025 AI Features

The database is no longer just a store. It is the AI.
SQL Server 2025 ships with native vector types, built-in LLM calls, and REST endpoint invocation directly from T-SQL — collapsing the boundary between your data layer and your AI stack. For developers and architects who already know SQL, this is the most important platform shift in a decade. This workshop is your guided, hands-on introduction to all of it.
Over eight focused hours, you will move from T-SQL fundamentals through the full arc of modern AI-adjacent development — writing production-grade code at every step, not watching someone else write it. Every module ends with a lab exercise built on the same schema, so by end of day you have a working semantic search application that runs entirely inside SQL Server, no application middleware required.
You Will Build a production-ready semantic product search engine — embedded entirely within the database engine — that ingests data, generates vector embeddings via Azure OpenAI, stores them in native VECTOR(1536) columns, retrieves results using VECTOR_DISTANCE(), generates natural-language summaries with AI_GENERATE(), and logs every AI inference call to a structured audit table with retry logic, row-level security, and dynamic data masking on sensitive columns.
Foundations
• Rewriting row-by-row CURSOR logic as set-based operations and measuring the improvement on actual execution plans
• Using window functions (ROW_NUMBER, RANK, LAG, LEAD, CUME_DIST, PERCENTILE_CONT) for trend analysis and ranking without self-joins
• Choosing correctly between CTEs, temp tables, table variables, and subqueries based on row count, statistics requirements, and plan reuse
Modern T-SQL
• Parsing and producing JSON with OPENJSON, FOR JSON PATH, JSON_ARRAY(), and JSON_OBJECT()
• Implementing system-versioned temporal tables for point-in-time audit queries with zero trigger code
• Using SQL Server's native graph extensions (NODE, EDGE, MATCH, SHORTEST_PATH) for relationship traversal
SQL Server 2025 & Cross-Platform AI
• Calling any HTTPS REST endpoint — Azure OpenAI, Microsoft Foundry, Ollama local models — directly from T-SQL using sp_invoke_external_rest_endpoint
• Storing and querying high-dimensional embeddings with the native VECTOR(n) data type and VECTOR_DISTANCE() across cosine, dot product, and Euclidean metrics
• Invoking language models natively with AI_GENERATE() for row-level summarization, classification, and entity extraction
• Scoring ONNX machine learning models in-database with PREDICT() — zero data movement, no Python runtime
• Navigating capability differences between SQL Server 2025, Azure SQL Database, and Microsoft Fabric SQL Analytics Endpoint for teams running workloads across multiple platforms
Performance & Production Readiness
• Accelerating analytics workloads with clustered columnstore indexes and approximate aggregations (APPROX_COUNT_DISTINCT, APPROX_PERCENTILE_DISC)
• Designing embedding tables with DiskANN approximate nearest-neighbor indexes and pre-filter strategies that reduce distance computation by orders of magnitude
• Implementing defense-in-depth security: Transparent Data Encryption, Always Encrypted column-level cryptography, Dynamic Data Masking, and Row-Level Security in a single multi-tenant schema
• Writing production stored procedures with structured TRY/CATCH error handling, idempotent retry logic, and full AI audit trails
This session is designed for database developers, data engineers, and solution architects with working T-SQL knowledge who are ready to build AI-powered applications directly on Microsoft SQL platforms. It is a strong preparation session for the DP-800: Designing and Implementing AI-Enabled Database Solutions certification exam.

Implementing Medallion Architecture in Microsoft Fabric

Medallion Architecture was developed to solve problems that have plagued data orchestration processes since last century. In this session, you will learn not only how to implement Medallion Architecture in Microsoft Fabric, but also why the architecture matters in real-world enterprise solutions and how it can solve the problems that curse other architectures. We will explore how separating raw ingestion, cleansing, and business-ready analytics reduces data duplication, improves trust in reporting, simplifies troubleshooting, and creates a repeatable framework for data engineering teams. You will understand the workspace and security organization necessary to implement data into structured Bronze, Silver, and Gold layers that improve data quality, governance, scalability, and reporting performance over time.
We will also discuss practical implementation decisions so you will understand the workspace design, orchestration patterns, data sharing, shortcuts, security boundaries, governance considerations, and deployment in Fabric with for both lakehouses and warehouses. By the end of the session, attendees will understand both the technical implementation and the architectural reasoning behind Medallion.

Using Vectors with SQL Server 2025 and GitHub Copilot Chat

Demonstrate stored procedures generating and embedding vectors in SQL Server 2025 tables with connections to Azure Open AI Models

Retool + Fabric SQL: Building a HITL Review Workflow

Human in the loop is a critical design element often overlooked. We demonstrate some ways to keep apprised of what your AI workflows are doing (with or without you).

Duration: 120 min lunch and learn

The Overton Window Company - Full Stack

Explore development of a Fabric SQL Database, Reactive UI, business logic, and operational reporting to support a fictitious contracting company that installs energy efficient windows.

Fabric Foundations: The Adhoc Lakehouse in Action

A guided tour of Microsoft Fabric’s Lakehouse, showcasing ingestion, transformation, and reporting using real-world files and analytics examples. A FabricDemo Workspace is set up for live demonstrations.

Instead of setting up connections to PowerBI Desktop, SFTP, Sharepoint, local file systems, and other file shares, just upload, make tables, and run queries in minutes. Duration: 45 min

Word Up: Automating Docs with Azure SQL + Power Automate

Learn how to generate branded Word documents from Azure SQL using Power Automate and Office Scripts. Includes mail merge, templating, and dynamic content injection.

Duration: 60 min

Excel as a Live Dashboard: Operational Reporting from Fabric SQL

Build dynamic Excel dashboards that connect directly to Fabric SQL. Explore pivot tables, slicers, and real-time refresh for operations teams.

Duration: 60 min

SSMS Deep Dive: Query Optimization & Metadata Discovery

Go beyond SELECT * — explore advanced T-SQL, CTEs, indexing, and metadata queries to accelerate development and validation.

Duration: 60 min

Fabric Lakehouse: Ingestion Patterns for Handling Real World Data Glitches

Build and monitor Fabric Data Factory pipelines to load CSVs with variable schemas and data types standardizing on PySpark and T-SQL notebooks for ingestion and error handling. You will leave with a methodology to load data even when it is duplicated, malformed or columns change.

Duration: 45 min

Real-Time Intelligence: From Stream to Dashboard

Use Fabric Eventhouse and KQL to build real-time monitoring dashboards. Ideal for anomaly detection and operational alerting.

Duration: 75 min

Power BI + Fabric: Semantic Models for the Enterprise

Build a Power BI model on top of Fabric Lakehouse. Includes DAX, relationships, and workspace governance.

Duration: 90 min

Document Intelligence: Automating PDF Ingestion from Blob to SQL

Automate the extraction of structured data from PDFs using Azure AI Document Intelligence and store results in Azure SQL.

Duration: 90 min

Fabric Security & Governance: Purview, Lineage, and Catalogs

Explore how to manage data access, lineage, and classification using Microsoft Purview and OneLake Data Catalog.

Duration: 60 minute overview

Microsoft Fabric for Power BI Users

Intro to Fabric Workspaces with demos of loading, joining, querying, and reporting from Lakehouse and Warehouse

Duration: 90 minutes

Lakehouse to Intelligence: Build AI-Ready Data Products with Fabric, SQL, and Spark

Build an AI-ready data product natively in Microsoft Fabric. Use OneLake and Delta Lake to shape lakehouse data with Spark, query it through the SQL analytics endpoint, and prepare trusted context for AI experiences. This full-day workshop emphasizes practical architecture choices, engine boundaries, governance, and production-minded patterns for data engineers and architects.

This workshop is designed for intermediate data professionals. Familiarity with SQL and basic data engineering concepts is recommended. Prior Microsoft Fabric experience is helpful but not required. Attendees should bring a laptop and be prepared to work hands-on with Microsoft Fabric, notebooks, Spark, SQL, and AI services. Access requirements and environment setup instructions will be provided before the workshop.

Python Meets SQL in Microsoft Fabric: Elmington's Data & AI Migration Story

What happens when Python engineers and SQL analytics teams meet in Microsoft Fabric? Using Elmington’s Fabric migration as a real-world case study, we’ll explore a modern OneLake architecture spanning Bronze ingestion, Silver Materialized Lake Views, and Gold analytics. See how Python, SQL, Lakehouse, and Fabric work together to modernize existing data workloads while letting each team build on the skills they already have to accelerate data refreshes exponentially.

A working knowledge of SQL, Python, data engineering, analytics, or Microsoft Fabric will help, but deep expertise is not required. The session is designed for data professionals who understand traditional data platforms and want to see how those skills translate into a modern Fabric architecture.

Shadow IT in the Era of AI: How Rayfin + Fabric Apps Become the New Access Database (That Scales)

Shadow IT is exploding as teams adopt AI faster than central IT can govern. This session shows how Rayfin SDK, Fabric Apps, Fabricator, and Power BI give departments an Access‑like toolkit that’s secure, governed, and scalable—turning ad‑hoc solutions into enterprise‑grade apps without slowing innovation.

Basic familiarity with data workflows in Fabric or Power BI is helpful but not required. If you can describe a business problem, you can build with Rayfin, Fabric Apps, and Fabricator—no deep coding background needed.

Departmental analysts, citizen developers, architects, and IT leaders who want to replace unmanaged Shadow IT with secure, scalable, governed micro‑apps built on the Microsoft data stack.

Modern app creation in the AI era rewards people who ask good questions, explore patterns, and iterate quickly. Rayfin and Fabric Apps abstract the heavy engineering—meaning curiosity, not coding, is what gets your solution into production. The teams that succeed aren’t the ones who know the most syntax; they’re the ones who keep pulling the thread until the real business problem is solved.