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Duration 21 hours
Course Outline
Introduction to AI-Enhanced SQL
- Overview of integrating AI into modern data systems
- The shift from traditional SQL to AI-assisted querying paradigms
- Key enterprise use cases and associated business benefits
Understanding LLMs within the SQL Context
- Mechanisms by which LLMs interpret and produce structured queries
- Evaluating GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL-specific tasks
- Techniques for fine-tuning models for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectural approaches and strategies for NL2SQL implementation
- Development and deployment of text-to-SQL pipelines
- Methods for assessing query accuracy and understanding 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 tools into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access controls for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Establishing AI governance frameworks 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-based architectures
Practical Implementation Labs
- Configuring AI-SQL connections and preparing test environments
- Generating and critically evaluating AI-produced queries
- Quantifying performance gains achieved through AI optimization
Future Trends and Enterprise Adoption Strategies
- The emergence of AI-native database systems and the evolution of SQL
- Integrating solutions with data lakes, BI tools, and data pipelines
- Developing internal AI query assistants to support organizational needs
Requirements
- A solid grasp of SQL fundamentals
- Practical experience in database administration or data engineering
- Familiarity with core AI and machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics team leads
- Teams focused on AI integration and platform engineering
