# AnomalyArmor > Intelligent data quality monitoring for data engineering teams. Detects schema changes, freshness issues, and data anomalies across Snowflake, Databricks, PostgreSQL, BigQuery, Redshift, ClickHouse, MySQL, SQL Server, and AWS Athena. AI-powered with natural language Q&A. Pricing starts at $5/table/month. ## Getting Started - [Introduction](https://docs.anomalyarmor.ai/introduction): What AnomalyArmor does and how it works - [Quickstart](https://docs.anomalyarmor.ai/quickstart/overview): Connect a database, run discovery, set up alerts - [How It Works](https://docs.anomalyarmor.ai/how-it-works): Architecture and data flow ## Data Sources - [Overview](https://docs.anomalyarmor.ai/data-sources/overview): Supported databases and connection methods - [PostgreSQL](https://docs.anomalyarmor.ai/data-sources/postgresql) - [Snowflake](https://docs.anomalyarmor.ai/data-sources/snowflake) - [Databricks](https://docs.anomalyarmor.ai/data-sources/databricks) - [BigQuery](https://docs.anomalyarmor.ai/data-sources/bigquery) - [Redshift](https://docs.anomalyarmor.ai/data-sources/redshift) - [ClickHouse](https://docs.anomalyarmor.ai/data-sources/clickhouse) - [MySQL](https://docs.anomalyarmor.ai/data-sources/mysql) - [SQL Server](https://docs.anomalyarmor.ai/data-sources/sql-server) ## Core Features - [Schema Monitoring](https://docs.anomalyarmor.ai/schema-monitoring/overview): Detect column additions, removals, and type changes - [Schema Drift Detection](https://docs.anomalyarmor.ai/schema-monitoring/schema-drift): Track structural changes over time - [Freshness Monitoring](https://docs.anomalyarmor.ai/data-quality/freshness-monitoring): Alert when data stops arriving on schedule - [Row Count Monitoring](https://docs.anomalyarmor.ai/data-quality/row-count-monitoring): Detect volume anomalies - [Data Quality Metrics](https://docs.anomalyarmor.ai/data-quality/metrics): Null rates, distinct counts, custom metrics - [Report Badges](https://docs.anomalyarmor.ai/data-quality/report-badges): Embeddable SVG status indicators ## Alerts - [Alert Rules](https://docs.anomalyarmor.ai/alerts/alert-rules): Configure what triggers alerts - [Slack](https://docs.anomalyarmor.ai/alerts/destinations/slack): Slack channel integration - [PagerDuty](https://docs.anomalyarmor.ai/alerts/destinations/pagerduty) - [Email](https://docs.anomalyarmor.ai/alerts/destinations/email) - [Webhooks](https://docs.anomalyarmor.ai/alerts/destinations/webhooks) - [Schedules & Blackouts](https://docs.anomalyarmor.ai/alerts/schedules): Operating hours and suppression windows ## AI Intelligence - [Overview](https://docs.anomalyarmor.ai/intelligence/overview): AI-powered data analysis - [Ask Questions](https://docs.anomalyarmor.ai/intelligence/ask-questions): Natural language Q&A about your data - [Use Cases](https://docs.anomalyarmor.ai/intelligence/use-cases) ## AI Agents & Integrations - [AI Agents Overview](https://docs.anomalyarmor.ai/ai-agents/overview): MCP server, Claude Code, Cursor integration - [MCP Server](https://docs.anomalyarmor.ai/integrations/mcp-server): Model Context Protocol integration - [Agent Skills](https://github.com/anomalyarmor/agents): 14 installable `/armor:*` skills for Claude Code, Cursor, Windsurf, Codex, and 40+ agents. Install via `npx skills add anomalyarmor/agents` ([skills.sh](https://skills.sh)) - [Claude Code](https://docs.anomalyarmor.ai/integrations/claude-code) - [Cursor](https://docs.anomalyarmor.ai/integrations/cursor) - [dbt](https://docs.anomalyarmor.ai/integrations/dbt): Data lineage via dbt - [Airflow](https://docs.anomalyarmor.ai/integrations/airflow) - [GitHub Actions](https://docs.anomalyarmor.ai/integrations/github-actions) ## Developer Tools - [Python SDK](https://docs.anomalyarmor.ai/sdk/overview): pip install anomalyarmor - [CLI](https://docs.anomalyarmor.ai/cli/overview): Command-line interface - [REST API](https://docs.anomalyarmor.ai/api/overview): Full API reference ## Pricing - [Plans](https://docs.anomalyarmor.ai/billing/plans): Starter ($15/table), Growth ($7/table), Professional ($5/table), Enterprise (custom) - [Pricing Page](https://www.anomalyarmor.ai/pricing) ## Security - [Security Overview](https://docs.anomalyarmor.ai/security/overview) - [Data Handling](https://docs.anomalyarmor.ai/security/data-handling): Metadata-only, never accesses PII - [Data Retention](https://docs.anomalyarmor.ai/security/data-retention): 90-day history, 1-year audit logs ## Blog - [Blog Home](https://blog.anomalyarmor.ai): Data engineering insights, tutorials, and product updates ### Published Posts - [Why I Built AnomalyArmor](https://blog.anomalyarmor.ai/why-i-built-anomalyarmor/): AnomalyArmor automates alerts for schema changes, freshness, and data quality. It's affordable, scalable, and user-friendly - [Schema Drift: The Silent Pipeline Killer](https://blog.anomalyarmor.ai/schema-drift-the-silent-pipeline-killer/): Schema drift is when your database schema changes without downstream systems knowing. Column renames, type changes, and dropped tables silently break pipelines while your monitoring stays green. Le... - [Data Quality Tools in 2026: What to Actually Look For](https://blog.anomalyarmor.ai/data-quality-tools-in-2026-what-to-actually-look-for/): Every data quality vendor checks the same feature boxes. What actually separates good tools from mediocre ones is time to value, noise control, pricing transparency, and workflow integration. Here'... - [Why We Open-Sourced Our Database Query Layer](https://blog.anomalyarmor.ai/why-we-open-sourced-our-database-query-layer/): Most data quality tools ask you to trust them with database access. AnomalyArmor's Query Security Gateway is open source, so you can verify exactly what queries we run. Choose from three access lev... - [AI Data Quality Monitoring: Tactical AI vs Strategic AI](https://blog.anomalyarmor.ai/ai-data-quality-monitoring-tactical-ai-vs-strategic-ai/): Most "AI-powered" data quality tools just add a chat interface to a metadata catalog. That's tactical AI. Strategic AI doesn't wait for your questions. It investigates root causes, recognizes patte... - [Set Up Data Quality Monitoring in Under 10 Minutes](https://blog.anomalyarmor.ai/set-up-data-quality-monitoring-in-under-10-minutes/) - [State of Data Engineering 2026](https://blog.anomalyarmor.ai/state-of-data-engineering-2026/): The 2026 State of Data Engineering Survey found that data teams spend 34% of their time on data quality and 26% firefighting. That's 60% of the week reacting to problems. The numbers reveal a vicio... - [Data Anomaly Detection: The Complete Guide for Data Engineers](https://blog.anomalyarmor.ai/data-anomaly-detection-the-complete-guide-for-data-engineers/): Data anomaly detection identifies data points that deviate from expected behavior. This guide covers the four types of anomalies, detection algorithms, and how to implement them in Snowflake, Datab... - [Data Quality Monitoring for Snowflake and Databricks: A Practical Guide](https://blog.anomalyarmor.ai/data-quality-monitoring-for-snowflake-and-databricks-a-practical-guide/): Schema changes, stale tables, and anomalies break Snowflake and Databricks pipelines silently. Here's how data quality monitoring works on both platforms, where built-in tools fall short, and how t... - [Data Freshness Monitoring: How to Detect Stale Data Before It Breaks Dashboards](https://blog.anomalyarmor.ai/data-freshness-monitoring-how-to-detect-stale-data-before-it-breaks-dashboards/): Data freshness monitoring checks whether your tables are updating on schedule and alerts you when they stop. Learn how to set freshness SLAs, why orchestration alerts aren't enough, and why DIY mon... - [Data Observability vs Data Quality: What's the Difference and Do You Need Both?](https://blog.anomalyarmor.ai/data-observability-vs-data-quality-whats-the-difference-and-do-you-need-both/): Data observability monitors pipeline health (freshness, volume, schema changes). Data quality validates the data itself (accuracy, completeness, consistency). Most teams need both. - [Data Pipeline Monitoring: How to Stop Silent Failures Before They Hit Production](https://blog.anomalyarmor.ai/data-pipeline-monitoring-how-to-stop-silent-failures-before-they-hit-production/): Data pipeline monitoring catches silent failures that orchestration tools miss. Learn the 5 types of pipeline failures, why Airflow alerts aren't enough, and how to set up monitoring that actually ... - [You Don't Need to Write Data Tests](https://blog.anomalyarmor.ai/you-dont-need-to-write-data-tests/): Data engineers know they should test their pipelines. They just don't have time. The real answer isn't better testing frameworks. It's automated data testing that works without writing a single test. - [Using AI to Set up Schema Drift Detection](https://blog.anomalyarmor.ai/using-ai-to-set-up-schema-drift-detection/): Set up schema drift observability in seconds - [The 6 Dimensions of Data Quality: Definitions, Examples, and How to Monitor Each](https://blog.anomalyarmor.ai/the-6-dimensions-of-data-quality-definitions-examples-and-how-to-monitor-each/): The six dimensions of data quality are accuracy, completeness, consistency, timeliness, validity, and uniqueness. This guide defines each dimension with SQL examples and shows how to monitor all si... ## Company - [About](https://www.anomalyarmor.ai/about) - [Contact](https://www.anomalyarmor.ai/about/contact) - [Status Page](https://status.anomalyarmor.ai)