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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within modern data systems
- The transition from traditional SQL to AI-assisted querying
- Key enterprise use cases and associated benefits
Understanding LLMs in a SQL Context
- Mechanisms by which LLMs interpret and generate structured queries
- Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications
- Strategies for fine-tuning models for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectural patterns and approaches for NL2SQL implementation
- Steps for building and deploying robust text-to-SQL pipelines
- Methods for evaluating query accuracy and aligning with user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and rectify inefficient queries
- Utilizing LLM-based query rewriting to enhance performance
- Integrating AI optimization into PostgreSQL and SQL Server environments
Security, Governance, and Auditability
- Strategies for controlling access to AI-generated queries
- Safeguarding explainability and ensuring regulatory compliance
- Implementing robust AI governance within enterprise data systems
LLM Integration and Orchestration
- Establishing connections between SQL engines and AI APIs
- Utilizing frameworks such as LangChain and LlamaIndex
- Deploying AI components across hybrid and cloud architectures
Practical Implementation Labs
- Configuration of AI-SQL connections and sandbox test environments
- Creation and rigorous evaluation of AI-generated queries
- Quantifying performance improvements through AI optimization
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native database systems and the evolution of SQL
- Seamless integration with data lakes, BI tools, and data pipelines
- Developing internal AI query assistants for organizational use
Requirements
- A solid grasp of SQL fundamentals
- Hands-on experience in database administration or data engineering
- Familiarity with core AI or machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leaders
- AI integration and platform engineering teams
