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 Duration 21 hours

Course Outline

Introduction to AI-Enhanced SQL

  • Overview of AI integration within data systems
  • The progression from traditional SQL to AI-assisted querying
  • Major enterprise use cases and associated benefits

Exploring LLMs in the SQL Context

  • Mechanisms by which LLMs interpret and generate structured queries
  • Comparative analysis of GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL tasks
  • Fine-tuning models for effective database interaction

Natural Language to SQL (NL2SQL) Architectures

  • Structural and methodological approaches to NL2SQL
  • Construction and deployment of text-to-SQL pipelines
  • Assessing query accuracy and alignment with user intent

AI-Powered Query Optimization

  • Leveraging AI to identify and rectify inefficient queries
  • Employing LLMs for query rewriting to enhance performance
  • Incorporating AI optimization into 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

  • Linking 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 setting up test environments
  • Generating and evaluating AI-produced queries
  • Quantifying performance gains through AI optimization

Future Trends and Enterprise Adoption Strategies

  • Emerging AI-native database systems and the evolution of SQL
  • Integration with data lakes, BI tools, and data pipelines
  • Developing internal AI query assistants for organizational use

Requirements

  • A solid grasp of SQL fundamentals
  • Professional experience in database administration or data engineering
  • Foundational 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

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