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Duration 21 hours
Course Outline
Introduction to AI-Enhanced SQL
- Overview of AI integration within data systems
- The transition from conventional SQL to AI-assisted querying
- Key enterprise applications and associated benefits
Understanding LLMs in the SQL Context
- How LLMs parse and generate structured queries
- Comparing GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL use cases
- Model fine-tuning for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectures and methodologies for NL2SQL
- Developing and deploying text-to-SQL pipelines
- Assessing query accuracy and alignment with user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and resolve inefficient queries
- Using LLMs to rewrite queries for improved performance
- Integrating AI optimization with PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access controls for AI-generated queries
- Safeguarding explainability and regulatory compliance
- Establishing AI governance frameworks in enterprise data systems
LLM Integration and Orchestration
- Bridging 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 testing environments
- Generating and assessing AI-created 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
- Interoperability with data lakes, BI tools, and data pipelines
- Developing internal AI query assistants for organizational use
Requirements
- A solid grasp of SQL fundamentals
- Background 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
- Teams focused on AI integration and platform engineering