Automated Data Profiling
Automatically profile your data sources and detect data quality issues, schema changes, and statistical anomalies with minimal configuration.
Drift Detection
Continuously monitor your data for statistical drift and changes over time. Get alerted when data quality degrades or unexpected patterns emerge.
Python-First
Built for Python data teams. Easy integration with SQLAlchemy and popular data orchestration tools like Dagster and dbt.
Advanced Statistical Tests
Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI), Chi-square, Entropy, and more for rigorous drift detection. Type-specific thresholds reduce false positives.
Anomaly Detection
Automatically detect outliers and seasonal anomalies using learned expectations with multiple detection methods (IQR, MAD, EWMA, trend/seasonality, regime shift).
Multi-Database Support
Works seamlessly with PostgreSQL, Snowflake, SQLite, MySQL, BigQuery, and Redshift. Unified API across all supported databases.
Web Dashboard
Lightweight local web dashboard (FastAPI + Next.js) for visualizing profiling runs and drift detection. Get insights at a glance.
CLI & API
Comprehensive command-line interface and powerful querying API for profiling runs, drift events, and table history. Perfect for automation and integration.
Built for Production
Every feature is designed to meet the demands of production workloads and enterprise requirements.
Expectation Learning
Automatically learns expected metric ranges from historical profiling data, including control limits, distributions, and categorical frequencies for proactive anomaly detection.
Event & Alert Hooks
Pluggable event system for real-time alerts and notifications on drift, schema changes, anomalies, and profiling lifecycle events. Integrate with Slack, email, or custom systems.
Partition-Aware Profiling
Intelligent partition handling with strategies for latest, recent_n, or sample partitions. Optimize profiling for large partitioned datasets.
Use Cases
See how teams are using Baselinr to solve real-world data quality challenges.
Data Quality Monitoring
Track data quality metrics over time and automatically detect when data quality degrades. Set up alerts for critical drift events.
Schema Change Detection
Automatically detect schema changes in your databases. Get notified when columns are added, removed, or modified.
Statistical Drift Detection
Identify statistical anomalies in your data using advanced tests. Detect distribution shifts, value range changes, and frequency variations.
Ready to Get Started?
Join developers building better data quality monitoring with Baselinr.