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 Duration 21 hours

Course Outline

Introduction to AI-Augmented SQL

  • An overview of AI integration within data systems
  • The evolution from traditional SQL to AI-assisted querying
  • Key enterprise use cases and associated benefits

Understanding LLMs in SQL Context

  • Mechanisms for LLM interpretation and generation of structured queries
  • Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications
  • Techniques for fine-tuning models for optimal database interaction

Natural Language to SQL (NL2SQL) Systems

  • Architectural frameworks and approaches for NL2SQL
  • Processes for building and deploying text-to-SQL pipelines
  • Methods for evaluating query accuracy and user intent

AI-Assisted Query Optimization

  • Leveraging AI to identify and rectify inefficient queries
  • LLM-based query rewriting strategies for enhanced performance
  • Integrating AI optimization into PostgreSQL and SQL Server

Security, Governance, and Auditability

  • Strategies for controlling access to AI-generated queries
  • Ensuring explainability and regulatory compliance
  • Implementing robust AI governance in enterprise data systems

LLM Integration and Orchestration

  • Establishing connections between SQL engines and AI APIs
  • Utilizing frameworks such as LangChain and LlamaIndex
  • Deploying AI components within hybrid and cloud architectures

Practical Implementation Labs

  • Configuration of AI-SQL connections and test environments
  • Creation and evaluation of AI-generated queries
  • Measuring performance gains through AI optimization

Future Trends and Enterprise Adoption Strategies

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

Requirements

  • A solid grasp of SQL fundamentals
  • Practical 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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