Automated Setup
Zero-touch configuration—automatically recommends tables and columns to monitor, suggests data quality checks, and can auto-apply configurations. Or configure everything manually with full control—your choice.
Comprehensive Monitoring
Profile your data, detect schema and statistical drift, identify anomalies, validate data quality rules, and perform root cause analysis—all in one platform.
Developer-First
Built for data engineers who want transparency and control. CLI-first with Python SDK, YAML/JSON configuration, and native integrations with dbt, Dagster, and Airflow.
Automated Profiling
Continuously profile your data warehouse with column-level metrics, distributions, and schema tracking. Intelligent table discovery reduces configuration overhead.
Drift Detection
Detect schema and statistical drift using multiple strategies with type-specific thresholds. Advanced statistical tests (KS, PSI, Chi-square) for rigorous detection.
Anomaly Detection
Automatically detect outliers and seasonal anomalies using learned expectations with multiple detection methods (IQR, MAD, EWMA, trend/seasonality, regime shift).
Data Validation
Rule-based data quality validation with built-in validators for format, range, enum, null checks, uniqueness, and referential integrity. Custom validators supported.
Root Cause Analysis
Automatically correlate anomalies with pipeline runs, code changes, and upstream data issues using temporal correlation, lineage analysis, and pattern matching.
Multi-Database Support
Works seamlessly with PostgreSQL, Snowflake, SQLite, MySQL, BigQuery, and Redshift. Unified API across all supported databases.
Production-Ready Features
Every feature is designed to meet the demands of production workloads with enterprise-grade capabilities.
Expectation Learning
Automatically learns expected metric ranges from historical profiling data, including control limits, distributions, and categorical frequencies for proactive anomaly detection.
Web Dashboard & AI Chat
Interactive web dashboard for visualizing profiling runs and drift detection. AI-powered chat interface for natural language data quality investigation.
CLI & Python SDK
Comprehensive command-line interface and powerful Python SDK for programmatic access. Perfect for automation, integration, and custom workflows.
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.
Data Lineage
Multi-source lineage extraction from dbt, Dagster, SQL parsing, and query history. Visual lineage graphs with interactive exploration and drift impact analysis.
Use Cases
See how teams are using Baselinr to solve real-world data quality challenges.
Automated Data Quality Setup
Turn on comprehensive data quality monitoring with minimal effort. System automatically recommends tables and columns, suggests checks, and can auto-apply configurations.
Root Cause Investigation
When anomalies occur, automatically correlate with pipeline runs, code changes, and upstream data issues to identify root causes. AI-powered chat for interactive investigation.
Pipeline Integration
Integrate with Airflow, Dagster, and dbt to validate data quality in your pipelines. Fail builds when critical issues are detected. Native orchestration support.
Ready to Get Started?
Join developers building better data quality monitoring with Baselinr.