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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within data systems
- The transition from traditional SQL to AI-assisted querying
- Primary enterprise use cases and strategic benefits
Understanding LLMs in SQL Context
- Mechanisms for LLMs to 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 frameworks and methodologies for NL2SQL
- Development and deployment of text-to-SQL pipelines
- Assessment of query accuracy and alignment with user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and remediate inefficient queries
- Performance-focused query rewriting using LLMs
- Integration of AI optimization with PostgreSQL and SQL Server
Security, Governance, and Auditability
- Access control mechanisms for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Establishing AI governance protocols in enterprise data systems
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs
- Utilizing frameworks such as LangChain and LlamaIndex
- Deploying AI components across hybrid and cloud architectures
Practical Implementation Labs
- Configuring AI-SQL connections and test environments
- Generation and evaluation of AI-produced queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native database systems and SQL evolution
- Integration with data lakes, BI tools, and pipelines
- Development of internal AI query assistants for organizational use
Requirements
- Solid grasp of SQL fundamentals
- Practical experience in database administration or data engineering
- Familiarity with core AI and machine learning concepts
Audience
- Data engineers and database administrators
- Enterprise architects and analytics leaders
- AI integration and platform engineering teams
