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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within data systems
- Transition from traditional SQL to AI-assisted querying
- Key enterprise use cases and strategic benefits
Understanding LLMs in the SQL Context
- Mechanisms by which LLMs interpret and generate structured queries
- Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL tasks
- Techniques for fine-tuning models for database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectural patterns and approaches for NL2SQL
- Development and deployment of text-to-SQL pipelines
- Assessing query accuracy and user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and rectify inefficient queries
- LLM-based query rewriting to enhance performance
- Embedding AI optimization into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access controls for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Implementing AI governance frameworks in enterprise data systems
LLM Integration and Orchestration
- Establishing connections between SQL engines and AI APIs
- Utilizing frameworks like LangChain and LlamaIndex
- Deploying AI components across hybrid and cloud architectures
Practical Implementation Labs
- Configuring AI-SQL connections and testing environments
- Generating and assessing AI-created queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native database systems and SQL evolution
- Interoperability with data lakes, BI tools, and pipelines
- Developing internal AI query assistants for organizational use
Requirements
- Solid grasp of SQL fundamentals
- Professional experience in database administration or data engineering
- Familiarity with basic AI or machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leaders
- AI integration and platform engineering teams
