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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within data systems
- The shift from traditional SQL to AI-assisted querying
- Key enterprise use cases and strategic benefits
Understanding LLMs in SQL Context
- How LLMs interpret and generate structured queries
- Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications
- Fine-tuning models for effective database interaction
Natural Language to SQL (NL2SQL) Systems
- Architectures and methodologies for NL2SQL
- Building and deploying robust text-to-SQL pipelines
- Evaluating query accuracy and alignment with user intent
AI-Assisted Query Optimization
- Utilizing 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
- Implementing robust AI governance within 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, testing, and evaluating AI-generated queries
- Measuring performance gains through AI optimization
Future Trends and Enterprise Adoption Strategies
- The evolution of SQL towards AI-native database systems
- Integration with data lakes, BI tools, and data pipelines
- Developing internal AI query assistants for organizational use
Requirements
- Solid understanding of SQL fundamentals
- Practical 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
- Teams focused on AI integration and platform engineering