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 Duration 21 hours

Course Outline

Introduction to AI-Augmented SQL

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

Understanding LLMs in the SQL Context

  • How LLMs interpret and generate structured queries
  • Comparing GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL-specific applications
  • Fine-tuning models to enhance database interaction

Natural Language to SQL (NL2SQL) Systems

  • Architectures and methodologies for NL2SQL
  • Constructing and deploying text-to-SQL pipelines
  • Assessing query accuracy and alignment with user intent

AI-Assisted Query Optimization

  • Utilizing AI to identify and rectify inefficient queries
  • Applying LLM-based query rewriting to boost performance
  • Embedding 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

  • Connecting SQL engines with AI APIs
  • Leveraging frameworks such as LangChain and LlamaIndex
  • Deploying AI components across hybrid and cloud architectures

Practical Implementation Labs

  • Configuring AI-SQL connections and test environments
  • Generating and evaluating AI-created queries
  • Quantifying performance gains achieved 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 organizational use

Requirements

  • Familiarity with core SQL fundamentals
  • Practical experience in database administration or data engineering
  • Foundational knowledge of AI and machine learning concepts

Target Audience

  • Data engineers and database administrators
  • Enterprise architects and analytics leads
  • Teams specializing in AI integration and platform engineering

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