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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration in data systems
- The evolution from traditional SQL to AI-assisted querying
- Key enterprise use cases and benefits
Understanding LLMs in the SQL Context
- How LLMs interpret and generate structured queries
- Comparison of GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL applications
- Fine-tuning models for database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectures and approaches for NL2SQL
- Building and deploying text-to-SQL pipelines
- Evaluating query accuracy and user intent
AI-Assisted Query Optimization
- Using AI to identify and correct inefficient queries
- LLM-based query rewriting for performance enhancement
- Integrating AI optimization into PostgreSQL and SQL Server
Security, Governance, and Auditability
- Managing access control for AI-generated queries
- Ensuring explainability and regulatory compliance
- Implementing AI governance in enterprise data systems
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs
- Leveraging frameworks such as LangChain and LlamaIndex
- Deploying AI components in hybrid and cloud architectures
Practical Implementation Labs
- Setting up AI-SQL connections and test environments
- Creating and evaluating AI-generated queries
- Measuring performance improvements through AI optimization
Future Trends and Enterprise Adoption Strategies
- AI-native database systems and the evolution of SQL
- Integration with data lakes, BI tools, and pipelines
- Building internal AI query assistants for organizations
Requirements
- A solid understanding of SQL fundamentals
- Experience in database administration or data engineering
- Basic knowledge of AI or machine learning concepts
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leads
- AI integration and platform engineering teams