Choosing Between DQLabs and Microsoft Purview?

Combines out-of-the-box, no-code custom rules, and AI-augmented custom data quality rules for complete depth and breadth of data quality coverage.

Provides a small set of built-in rule types and supports creating custom rules, but its out-of-the-box rule library is limited — complex or domain-specific checks require custom rule authoring.

Prizm computes asset criticality using lineage impact, usage, freshness, and business importance. Agents automatically focus profiling, quality checks, and alerts on active, high-impact data while de-prioritizing cold or low-risk assets.

Purview uses catalog metadata, basic quality metrics, and freshness scores to help users understand asset health, but it does not autonomously prioritize profiling/monitoring based on lineage impact, usage, freshness, or business importance.

Agents continuously decide when to run profiling and quality jobs and dynamically adjust profiling depth per attribute based on criticality and behavior—no static schedules required.

Purview supports scheduled scanning and rule evaluation via scanners/connectors, but execution timing and depth are static and user/configuration driven. It does not autonomously adjust schedules or profiling intensity based on changing data behavior or criticality.

Prizm ingests governance or compliance documents and uses LLMs to automatically extract glossary terms, domains, and enforceable data quality rules—operationalizing policies end-to-end.

Purview provides policy and rule authoring for classifications, glossary, and standards, but has no documented capability to ingest policy documents and use LLMs to automatically extract terms and generate enforceable data quality rules. Policy interpretation and rule creation remain manual or rule-editor driven.

Comprehensive coverage across on-prem, cloud, hybrid, and streaming environments; supports structured, unstructured, and streaming data with seamless adaptability.

Supports scanning and cataloging many Azure, cloud and on-prem sources via its scanners/connectors and can register and scan varied data stores; streaming/real-time DQ is not its primary focus — Purview is catalog/governance centric.

Offers extensive, granular metrics spanning data quality, lineage, drift, freshness, pipeline health, usage, and spend—fully contextualized for business insights.

Purview does not provide the same breadth of real-time pipeline health, drift detection, or cost/usage telemetry as an operational observability platform.

Auto-application of rules at the attribute, semantic, and business term level with minimal manual setup.

There is no documented, automatic inheritance mechanism that propagates a lookup/rule across all attributes sharing a semantic term. In practice, broader semantic-level rule inheritance is a manual/configuration task in Purview.

Provides comprehensive observability across multiple layers: data health and reliability, pipeline health, infrastructure, cost optimization, and data usage.

Purview offers observability primarily around data assets, including freshness, quality scores, and catalog-level asset health. Lacks pipeline, infrastructure, cost, and usage observability.

Offers comprehensive end-to-end lineage with an AI-powered business impact visualizer that maps data issues directly to KPIs, alongside advanced AI-generated root cause analysis summaries that significantly accelerate issue diagnosis and resolution.

Purview provides static lineage graphs and dependency views but does not include AI-powered RCA or KPI-level business impact analysis.

Goes beyond data changes, offering full observability and behavioral analytics for pipelines and IT infrastructure to drive proactive reliability and rapid anomaly detection.

Purview does not monitor pipelines or infrastructure; observability is centered on cataloged data assets, schema changes, and freshness insights from supported scanners.

Provides advanced cost analysis, historical spend trends, budget allocation, and seamless showback/chargeback for comprehensive FinOps execution.

Purview has no native cost observability or FinOps tracking. Cost intelligence must be obtained separately through Azure Cost Management—outside Purview’s data observability framework.

Animated, interactive lineage with time-travel, showing volume/freshness/schema drifts across temporal snapshots — enabling backward exploration.

Purview lineage is static and represents current state only. There is no time-travel lineage or historical schema/volume drift visualization in lineage graphs.

Employs an AI-driven Auto Semantics engine that automates metadata discovery, classification, business-term mapping, and context-aware rule application without manual mapping.

Automated business-term mapping is not fully automatic — terms must be manually associated or applied through patterns or classifiers. Context-aware rule application is not automated at semantic term level.

Rules linked to semantic terms propagate automatically across assets for rapid onboarding, auditability, and consistency.

Purview applies rules at table or column level. There is no native automatic rule inheritance across assets based on business terms or semantic meaning. Rules do not automatically propagate when a term is applied.