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Duration 21 hours
Course Outline
Foundations of AI-Augmented SQL
- Overview of integrating AI into data systems
- The transition from traditional SQL to AI-assisted querying
- Key enterprise use cases and associated benefits
Role of LLMs in SQL Environments
- 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 to interact with databases
Natural Language to SQL (NL2SQL) Architectures
- Architectural approaches and design patterns for NL2SQL
- Construction and deployment of text-to-SQL pipelines
- Assessing query accuracy and understanding user intent
AI-Driven Query Optimization
- Leveraging AI to identify and rectify inefficient queries
- Employing LLM-based query rewriting to enhance performance
- Incorporating AI optimization into PostgreSQL and SQL Server
Security, Governance, and Audit Trails
- Managing access controls for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Establishing AI governance frameworks in enterprise data systems
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs
- Utilizing frameworks such as LangChain and LlamaIndex
- Deploying AI components within hybrid and cloud-based 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 Strategy
- The rise of AI-native database systems and the evolution of SQL
- Interoperability with data lakes, BI tools, and data pipelines
- Developing internal 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 and machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leads
- AI integration and platform engineering teams
