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Duration 21 hours
Course Outline
Introduction to AI-Enhanced SQL
- The role of AI in modern data systems
- The shift from traditional SQL to AI-assisted querying
- Key enterprise use cases and strategic benefits
LLMs in the SQL Context
- Mechanisms for LLMs to interpret and generate structured queries
- Evaluating GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL tasks
- Refining models for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Core architectures and methodologies for NL2SQL
- Construction and deployment of text-to-SQL pipelines
- Assessing query accuracy and aligning with user intent
AI-Powered Query Optimization
- Leveraging AI to identify and resolve inefficient queries
- Using LLMs to rewrite queries for superior performance
- Embedding AI optimization into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access rights for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Establishing AI governance frameworks within enterprise data systems
LLM Integration and Orchestration
- Bridging SQL engines with AI APIs
- Utilizing frameworks like LangChain and LlamaIndex
- Rolling out AI components across hybrid and cloud architectures
Practical Implementation Labs
- Configuring AI-SQL connections and sandbox environments
- Generating and assessing AI-created queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native databases and the evolution of SQL
- Connecting with data lakes, BI tools, and data pipelines
- Developing in-house AI query assistants for organizational use
Requirements
- A solid grasp of SQL fundamentals
- Practical experience in database administration or data engineering
- Foundational knowledge of AI or machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leads
- Teams focused on AI integration and platform engineering
