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Duration 21 hours
Course Outline
Introduction to AI-Enhanced SQL
- Overview of AI integration within modern data systems
- The shift from traditional SQL to AI-assisted querying
- Key enterprise use cases and their strategic benefits
Understanding LLMs in a SQL Context
- How LLMs interpret and generate structured queries
- Comparing GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL applications
- Techniques for fine-tuning models for database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectural patterns and approaches for NL2SQL
- Building and deploying text-to-SQL pipelines
- Evaluating query accuracy and alignment with user intent
AI-Assisted Query Optimization
- Using AI to identify and rectify inefficient queries
- LLM-based query rewriting to enhance performance
- Integrating AI optimization into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access controls for AI-generated queries
- Ensuring model explainability and regulatory compliance
- Establishing AI governance frameworks in enterprise data systems
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs
- Leveraging 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-driven queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native database systems and the evolution of SQL
- Integrating with data lakes, BI tools, and data pipelines
- Developing internal AI query assistants for organizational use
Requirements
- Proficiency in SQL fundamentals
- Professional 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 leads
- AI integration and platform engineering teams