Kensu

Additional features

Real-time Data Monitoring

Continuously observes the quality and usage of your data as it moves through pipelines and resides in various systems, providing insights in real-time.

Agent-Based Deployment

Unique "shift-left" approach where lightweight agents are embedded directly into applications and data pipelines to collect observations at the source, offering deep, contextual visibility.

Data Quality Monitoring

Detects a wide range of data quality issues, including freshness (data timeliness), completeness (missing values), accuracy, consistency, and validity.

Data Lineage (Kensu Explorer)

Automatically maps the flow of data, showing its origin, transformations, and dependencies across your entire data ecosystem to provide context for issues and support impact analysis.

Schema Change Detection

Automatically identifies structural changes in data schemas that could impact downstream applications or analytics.

Data Profiling

Gathers statistics and insights about data at rest and in motion to understand its characteristics and automatically recommend monitoring rules.

Anomaly Detection

Uses AI and historical data to automatically identify unusual patterns or deviations in data behavior that indicate potential problems.

Custom Metrics & Rules

Allows data teams to define and implement specific monitoring rules and metrics tailored to their unique data quality expectations and business logic.

Instant Alerting (Kensu Notifier)

Sends immediate, contextual notifications when data quality rules fail or anomalies are detected, ensuring teams are aware of issues as soon as they arise.

Automated Ticketing (Kensu Tickets)

Automatically generates tickets in incident management systems, providing details on the affected data source and the nature of the problem for quick follow-up.

Root Cause Analysis (Kensu Explorer)

Leverages data lineage and detailed observations to pinpoint the exact underlying cause of data incidents, speeding up diagnosis.

Impacted System Identification

Clearly identifies all downstream systems, applications, and dashboards that are affected by a data quality issue.

Proactive Issue Prevention (Kensu Circuit Breaker)

Enables automatic "circuit breaking" or freezing of applications/workflows when critical data quality incidents are detected, preventing flawed data from propagating.

Reduced Resolution Times

Significantly cuts the time required to detect, diagnose, and fix data problems, often by up to 50%.

Increased Data Trust & Reliability

By ensuring data quality and providing transparent issue resolution, it builds confidence in data among all stakeholders (data scientists, DataOps, business users).

Hybrid/Multi-Cloud/On-Premise Support

One of the few platforms that can observe data across diverse environments, including on-premises, multiple cloud providers, and hybrid setups.

Azure Data Factory Integration

Offers strong, seamless integration to specifically observe and monitor data within Azure Data Factory pipelines.

Integration with Data Engineering & BI Tools

Designed to integrate with existing data tools, data catalogs, glossaries, and incident management systems.

Agent Remote Controller

Provides an intuitive user interface to configure and manage observability agents without requiring changes to application code.

Observability Dashboard

Offers a centralized dashboard to view project statuses, data health, and drill down into specific events.

Cost Savings

Helps reduce costs associated with data downtime, manual troubleshooting, and making business decisions based on erroneous data.