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Duration 21 hours
Course Outline
Foundations of AI-Augmented SQL
- Overview of AI integration within data systems
- The shift from traditional SQL to AI-assisted querying
- Key enterprise use cases and associated benefits
LLMs in a SQL Context
- How LLMs parse and generate structured queries
- Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL tasks
- Techniques for fine-tuning models for database interaction
Natural Language to SQL (NL2SQL) Architectures
- Architectural patterns and approaches for NL2SQL
- Development and deployment of text-to-SQL pipelines
- Assessing query accuracy and aligning with user intent
AI-Assisted Query Optimization
- Leveraging AI to identify and rectify inefficient queries
- LLM-based query rewriting to boost performance
- Embedding AI optimization into PostgreSQL and SQL Server
Security, Governance, and Audit Trails
- 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
Hands-on Implementation Labs
- Configuring AI-SQL connections and test environments
- Generating and assessing AI-created queries
- Quantifying performance gains through AI optimization
Future Trends and Enterprise Adoption
- The rise of AI-native database systems and the evolution of SQL
- Integration strategies with data lakes, BI tools, and pipelines
- Building bespoke AI query assistants for organizational use
Requirements
- Familiarity with core SQL concepts
- Practical experience in database administration or data engineering
- Fundamental understanding of AI or machine learning principles
Target Audience
- Data engineers and database administrators
- Enterprise architects and analytics leaders
- Teams focused on AI integration and platform engineering