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YAML-Based Configuration
Define your data quality checks with simple, human-readable YAML. No complex code required - just describe what you want to validate and Weiser handles the rest.
LLM-Friendly Design
Designed for the AI era. Large Language Models can easily understand and generate Weiser configurations, making it perfect for AI-assisted data quality management and automated check generation.
Enterprise-Ready Scale
From startup analytics to enterprise data warehouses. Supports PostgreSQL, Cube.js semantic layers, and scales to handle millions of records with advanced statistical analysis and anomaly detection.
Simple YAML Configuration
Define data quality checks with intuitive YAML syntax. Perfect for version control, team collaboration, and AI-assisted generation.
# weiser-config.yaml checks: - name: orders_exist dataset: orders type: row_count condition: gt threshold: 0 - name: revenue_validation dataset: orders type: sum measure: order_amount condition: ge threshold: 10000 filter: status = 'completed' - name: data_completeness dataset: customers type: not_empty_pct dimensions: [email, phone] condition: le threshold: 0.05 # Max 5% NULLLLM-Friendly Design
Weiser's human-readable configuration makes it perfect for AI assistance. LLMs can easily understand, generate, and modify data quality checks.
🤖 AI Code Generation
LLMs can generate Weiser configs from natural language descriptions
📝 Self-Documenting
YAML structure is inherently readable by both humans and AI
🔄 Easy Modification
AI assistants can update and refine existing configurations
💡 Smart Suggestions
LLMs can recommend new checks based on your data schema
