Collibra vs Bigeye

Collibra8.1Visit BBigeye7.4Visit

Higher score Collibra #5 in Data Testing Tools 8.1/10 Pricing on request ✓ 3 of 4 features Visit Collibra

vs

B Bigeye #8 in Data Testing Tools 7.4/10 Pricing on request ✓ 3 of 4 features Visit Bigeye

• Collibra fits best

  Organizations that want data quality checks tied directly to catalog, lineage, and ownership records

• Bigeye fits best

  Teams that want broad automated metric coverage without defining every check manually

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Side by side

Feature	Collibra 8.1/10 Visit ↗	BBigeye 7.4/10 Visit ↗
At a glance
Editor score	8.1	7.4
Ranking	#5 in Data Testing Tools	#8 in Data Testing Tools
Best for	Organizations that want data quality checks tied directly to catalog, lineage, and ownership records	Teams that want broad automated metric coverage without defining every check manually
Pricing model	Paid	Paid
Starting price	Pricing on request	Pricing on request
Free plan	—	—
Free trial	—	—
Features Collibra 3/4 · Bigeye 3/4
Automated anomaly detection	—	✓ (best)
Data lineage mapping	✓ (best)	—
Alerting integrations	✓	✓
API access	✓	✓
Specs
Deployment model	Hybrid	Cloud
Our review
Pros	Quality scores sit next to catalog entries, so a business user sees trust status and lineage in one place, not just engineers Strong ownership and stewardship workflows for assigning who's responsible when a check fails Long enterprise track record in regulated and highly governed industries	Large catalog of pre-built data quality metrics speeds up initial coverage across many tables at once Automated threshold detection reduces the amount of manual rule-tuning needed to get started Built specifically around scaling monitoring across many tables rather than a handful
Cons	Budgeting has to start blind, since no price list is published anywhere Teams that only want technical quality checks (no catalog, no stewardship workflow) are buying more than they need Rolling out the catalog and ownership model well takes real organizational effort, not just a technical install	What it costs stays unclear until sales gets involved; nothing is listed publicly Less governance/catalog depth than platforms built around stewardship workflows Pre-built metrics still need review to make sure they match what actually matters for a specific dataset
Our verdict	Collibra's quality features exist inside its catalog, which is the right starting point for an organization where "is this data trustworthy" needs to be answerable by a business stakeholder, not just an engineer reading a log. A quality… Read the review →	Bigeye's approach is to front-load a large catalog of pre-built metrics so a team gets broad coverage across many tables quickly, instead of hand-writing a check for each one before monitoring starts. Automated threshold detection on top… Read the review →

Strengths and trade-offs

• Collibra — where it wins

  • Quality scores sit next to catalog entries, so a business user sees trust status and lineage in one place, not just engineers
  • Strong ownership and stewardship workflows for assigning who's responsible when a check fails
  • Long enterprise track record in regulated and highly governed industries

  Where it doesn't

  • Budgeting has to start blind, since no price list is published anywhere
  • Teams that only want technical quality checks (no catalog, no stewardship workflow) are buying more than they need
  • Rolling out the catalog and ownership model well takes real organizational effort, not just a technical install

• Bigeye — where it wins

  • Large catalog of pre-built data quality metrics speeds up initial coverage across many tables at once
  • Automated threshold detection reduces the amount of manual rule-tuning needed to get started
  • Built specifically around scaling monitoring across many tables rather than a handful

  Where it doesn't

  • What it costs stays unclear until sales gets involved; nothing is listed publicly
  • Less governance/catalog depth than platforms built around stewardship workflows
  • Pre-built metrics still need review to make sure they match what actually matters for a specific dataset

• Collibra8.1/10 · Pricing on request

  Quality checks here are a feature of the catalog, which matters most to governance-led organizations.

  Visit CollibraFull verdict →
• BBigeye7.4/10 · Pricing on request

  Leans on a large library of pre-built metrics so a team isn't writing every check from scratch.

  Visit BigeyeFull verdict →