Metaplane vs OpenUBA

Metaplane is a cloud data observability platform for monitoring data quality and catching issues before they affect business operations. Its monitors cover volume, schema, freshness, uniqueness, nullness, statistical distribution, and custom SQL. Machine-learning anomaly detection accounts for seasonality and trends, and real-time detection is listed. Column-level lineage maps data from sources through to BI tools and is included in Pro and Enterprise. Data CI/CD previews downstream impact and tests changes in pull requests; it is an add-on in those plans. Integrations include Snowflake, BigQuery, Redshift, ClickHouse, Databricks, dbt, Tableau, Looker, Slack, Microsoft Teams, PagerDuty, and Jira. Metaplane says it accesses aggregate metadata rather than sensitive customer information. It runs on AWS, hosts servers in the U.S., uses recent TLS versions for connections, and encrypts databases at rest. A free plan is described as free forever, with 10 monitored tables, four users, and three custom SQL monitors; a 14-day trial is also offered.

OpenUBA is a free, open-source User and Entity Behavior Analytics framework for security analytics. It analyzes behavior with machine-learning models to identify anomalies, assign risk scores, and produce classifications. The dashboard covers entity monitoring, risk trends, job orchestration, case management, and real-time behavioral baselines for tracking 10,000+ users. Data ingestion supports Spark, Elasticsearch, and CSV, and the project describes the platform as SIEM agnostic. Rule Studio provides a visual flow-graph builder for detection logic, while matching rules can trigger real-time alerts. Cases can be triaged, escalated, and resolved in built-in workflows. Models use a model.yaml configuration and MODEL.py interface with train() and infer() methods; examples use scikit-learn, TensorFlow, PyTorch, Keras, and NetworkX. A Kubernetes-native operator runs jobs in ephemeral containers, with GraphQL logs and monitoring. Deployment is self-hosted on Kind or production Kubernetes clusters.