MS SQL with Generative AI
Description
This training introduces developers and data professionals to the integration of Generative AI with Microsoft SQL Server to enhance data interaction, automation, and intelligence. Participants will learn how to use LLMs to translate natural language into SQL queries, generate insights from structured data, and build intelligent data-driven applications. The course covers architectures that combine SQL Server with vector databases, APIs, and orchestration frameworks for Retrieval-Augmented Generation (RAG). It also explores how to automate reporting, anomaly detection, and decision support using AI-enhanced pipelines. Practical labs focus on building end-to-end solutions that connect SQL Server with modern AI services such as Azure OpenAI. Emphasis is placed on performance, security, and governance when integrating AI into enterprise data environments. By the end, participants will be able to design and implement production-ready systems that augment SQL-based workflows with intelligent capabilities.
Indicative Duration: 14 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. Strategic Overview of AI in Data Platforms | AI-Augmented Data Engineering
• Role of Generative AI in modern data platforms |
| 2. Advanced SQL & Data Modeling | Schema Design for AI-Driven Systems
• Star schemas |
| 3. AI-Assisted Query Optimization | Performance Engineering with AI
• Execution plans |
| 4. Semantic Layer & Data Abstraction | AI over Business Semantics
• Building semantic layers (facts, dimensions) |
| 5. Advanced Analytics with AI |
Complex Query Orchestration • Window functions |
| 6. Automation & Code Generation | AI for SQL Engineering
• Generating stored procedures |
| 7. RAG over Databases | Retrieval-Augmented SQL Systems
• Combining SQL Server with vector search |
| 8. Security, Governance & Risk | Safe AI Usage in SQL Systems
• Preventing SQL injection via AI |
| 9. Cost & Performance Optimization | Efficient AI + SQL Usage
• Reducing query cost |
| 10. Enterprise Use Cases | End-to-End AI Data Solutions
• Case studies (banking, retail, energy), building AI copilots for analytics |
Learning Objectives
Upon completion of the course participants will be able to:
- Design systems that integrate SQL Server with LLMs for intelligent data querying
- Implement natural language to SQL pipelines with validation and optimization
- Build RAG-based solutions combining structured and unstructured data sources
- Develop AI-driven dashboards and automated reporting workflows
- Apply best practices for security, governance, and performance in AI-augmented data systems
Target Audience
- Roles: Data Analyst, Business Intelligence Manager, Digital Transformation Manager
- Seniority: Mid to Senior Level
Prerequisite Knowledge
- Basic understanding of SQL querying, relational databases, and data modeling concepts
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
