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Data Governance

DQLabs

AI-native platform for data observability, quality and context

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About DQLabs

DQLabs offers the PRIZM platform that combines data observability, data quality and an enterprise context layer for discovery, classification, glossary and lineage. It uses agentless connections and ML-driven anomaly detection with root-cause analysis.

Common use cases

  • Monitor data pipelines
  • Score data quality
  • Prepare data for AI
  • Classify sensitive data

Products and services

PRIZM

Unified platform for observability, quality and context.

Data Observability

Monitors freshness, schema, volume, cost and performance.

Data Quality

Policy-driven rules with AI-suggested checks and scores.

Enterprise Context

Discovery, tagging, glossary and lineage.

Key features

  • Agentless coverage
  • Anomaly detection
  • No-code quality rules
  • Quality scoring
  • Alert root-cause analysis
  • Business glossary

Integrations

  • Snowflake
  • Databricks
  • Amazon Web Services
  • Microsoft Azure
  • Google Cloud
  • SAP
  • dbt
  • Airflow

DQLabs alternatives

FAQs

What does DQLabs do?

Data observability, data quality and metadata context in one platform.

What does it integrate with?

Cloud data platforms and tools including Snowflake, Databricks, dbt and Airflow.

Is it agentless?

Its site describes agentless coverage across sources.

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