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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within data systems
- The shift from traditional SQL to AI-assisted querying
- Key enterprise use cases and associated benefits
Understanding LLMs in SQL Context
- How LLMs interpret and generate structured queries
- Comparing GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL applications
- Fine-tuning models for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectural approaches for NL2SQL systems
- Constructing and deploying text-to-SQL pipelines
- Assessing query accuracy and user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and fix inefficient queries
- Using LLMs for query rewriting to enhance performance
- Integrating AI optimization into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access rights for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Implementing AI governance within enterprise data systems
LLM Integration and Orchestration
- Linking 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
- Generating and evaluating AI-created queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- Emerging AI-native database systems and the evolution of SQL
- Integration with data lakes, BI tools, and data pipelines
- Creating internal AI query assistants for organizations
Requirements
- A solid grasp of SQL fundamentals
- Practical experience in database administration or data engineering
- Familiarity with core AI or machine learning concepts
Audience
- Data engineers and database administrators
- Enterprise architects and analytics leads
- Teams focused on AI integration and platform engineering
