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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- The landscape of AI integration within data systems.
- The transition from traditional SQL to AI-assisted querying.
- Key enterprise use cases and associated benefits.
LLMs in the SQL Context
- Mechanisms by which LLMs interpret and generate structured queries.
- Evaluating GPT, LLaMA, DeepSeek, Qwen, and Mistral for SQL applications.
- Strategies for fine-tuning models to interact with databases effectively.
Natural Language to SQL (NL2SQL) Systems
- Core architectures and methodologies for NL2SQL.
- Building and deploying robust text-to-SQL pipelines.
- Assessing query accuracy and alignment with user intent.
AI-Assisted Query Optimization
- Leveraging AI to identify and rectify inefficient queries.
- Utilizing LLMs for query rewriting to enhance performance.
- Integrating AI optimization features into PostgreSQL and SQL Server.
Security, Governance, and Auditability
- Managing access controls for AI-generated queries.
- Ensuring model explainability and regulatory compliance.
- Establishing AI governance frameworks within enterprise data systems.
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs seamlessly.
- Utilizing frameworks such as LangChain and LlamaIndex.
- Deploying AI components across hybrid and cloud architectures.
Practical Implementation Labs
- Configuring AI-SQL connections and setting up test environments.
- Generating and evaluating AI-created queries.
- Quantifying performance gains through AI optimization.
Future Trends and Enterprise Adoption
- The evolution of SQL in AI-native database systems.
- Integration with data lakes, BI tools, and data pipelines.
- Developing internal AI query assistants for organizational use.
Requirements
- A solid grasp 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 responsible for AI integration and platform engineering.
