Everything you need to profile, monitor, and detect drift in your SQL databases. From automated profiling to advanced statistical tests, Baselinr has you covered.
Built with modern development practices and designed for scalability, reliability, and developer experience.
Profile tables with column-level metrics including count, null %, distinct values, mean, stddev, histograms, and more. Automatically compute comprehensive statistics for your datasets.
Compare profiling runs to detect schema and statistical drift with configurable strategies. Intelligent baseline selection automatically chooses the optimal comparison method.
Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI), Chi-square, Entropy, and more for rigorous drift detection. Type-specific thresholds reduce false positives.
Automatically detect outliers and seasonal anomalies using learned expectations with multiple detection methods (IQR, MAD, EWMA, trend/seasonality, regime shift).
Works seamlessly with PostgreSQL, Snowflake, SQLite, MySQL, BigQuery, and Redshift. Unified API across all supported databases.
Lightweight local web dashboard (FastAPI + Next.js) for visualizing profiling runs and drift detection. Get insights at a glance.
Comprehensive command-line interface and powerful querying API for profiling runs, drift events, and table history. Perfect for automation and integration.
Every feature is designed to meet the demands of production workloads and enterprise requirements.
Automatically learns expected metric ranges from historical profiling data, including control limits, distributions, and categorical frequencies for proactive anomaly detection.
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.
Intelligent partition handling with strategies for latest, recent_n, or sample partitions. Optimize profiling for large partitioned datasets.
See how teams are using Baselinr to solve real-world data quality challenges.
Track data quality metrics over time and automatically detect when data quality degrades. Set up alerts for critical drift events.
Automatically detect schema changes in your databases. Get notified when columns are added, removed, or modified.
Identify statistical anomalies in your data using advanced tests. Detect distribution shifts, value range changes, and frequency variations.
Join developers building better data quality monitoring with Baselinr.