Kensu

The Data Observability Company.

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Overview

Kensu provides data observability, which is crucial for a data mesh. As domain teams independently create and update their data products, Kensu allows them and their consumers to monitor the reliability, quality, and timeliness of the data. It generates alerts on data anomalies, schema changes, and pipeline failures, helping to build trust in the distributed data products.

✨ Key Features

  • Data Pipeline Monitoring
  • Data Quality and Anomaly Detection
  • Schema Drift Detection
  • Data Lineage
  • Proactive Alerting

🎯 Key Differentiators

  • Context-rich observability by integrating with the data applications themselves
  • Proactive, preventative approach
  • Agent-based deployment for deep visibility

Unique Value: Provides the necessary observability for a data mesh, ensuring that decentralized data products are trustworthy, reliable, and meet the quality standards expected by consumers.

🎯 Use Cases (4)

Ensuring the reliability of data products in a data mesh Proactively detecting and resolving data quality issues Providing data consumers with trust and visibility into data health Debugging data pipeline failures

✅ Best For

  • Monitoring SLAs for critical data products
  • Alerting downstream consumers of upstream data issues

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Data governance policy enforcement
  • Data access control

🏆 Alternatives

Monte Carlo Soda Datadog

Focuses on collecting metadata from the source of processing, providing more granular and real-time insights compared to tools that only scan data at rest.

💻 Platforms

Web API

🔌 Integrations

Apache Spark Airflow dbt Kafka Snowflake BigQuery

🛟 Support Options

  • ✓ Email Support
  • ✓ Live Chat
  • ✓ Dedicated Support (Enterprise tier)

🔒 Compliance & Security

✓ SOC 2 ✓ GDPR ✓ SSO ✓ SOC 2

💰 Pricing

Contact for pricing
Free Tier Available

✓ 14-day free trial

Free tier: Free edition available.

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