WrenAI OSS Deep Dive: Semantic Modeling, Text to SQL, and Guardrails Training Course
WrenAI is an open-source generative BI tool that enables natural language to SQL conversion and semantic data modeling.
This instructor-led, live training (online or onsite) is aimed at advanced-level data engineers, analytics engineers, and ML engineers who wish to build robust semantic layers, tune prompts, and ensure reliable SQL generation.
By the end of this training, participants will be able to:
- Implement semantic models for consistent metric definitions across teams.
- Optimize text-to-SQL performance for accuracy and scalability.
- Configure and enforce guardrails to avoid invalid or risky queries.
- Integrate WrenAI OSS into data pipelines and analytics workflows.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to WrenAI OSS
- Overview of WrenAI architecture
- Key OSS components and ecosystem
- Installation and setup
Semantic Modeling in Wren AI
- Defining semantic layers
- Designing reusable metrics and dimensions
- Best practices for consistency and maintainability
Text to SQL in Practice
- Mapping natural language to queries
- Improving SQL generation accuracy
- Common challenges and troubleshooting
Prompt Tuning and Optimization
- Prompt engineering strategies
- Fine-tuning for enterprise datasets
- Balancing accuracy and performance
Implementing Guardrails
- Preventing unsafe or costly queries
- Validation and approval mechanisms
- Governance and compliance considerations
Integrating WrenAI into Data Workflows
- Embedding Wren AI in pipelines
- Connecting to BI and visualization tools
- Multi-user and enterprise deployments
Advanced Use Cases and Extensions
- Custom plugins and API integrations
- Extending WrenAI with ML models
- Scaling for large datasets
Summary and Next Steps
Requirements
- Strong understanding of SQL and database systems
- Experience with data modeling and semantic layers
- Familiarity with machine learning or natural language processing concepts
Audience
- Data engineers
- Analytics engineers
- ML engineers
Open Training Courses require 5+ participants.
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