Top 25 Data Lineage Tools in 2025: Features, Strengths & How to Choose

As data estates become more fragmented and regulations like GDPR, BCBS 239, and HIPAA demand transparency, the need for accurate, automated data lineage is urgent. This guide profiles 25 leading tools (enterprise, mid-market, and open-source) to help teams evaluate features like column-level tracing, impact analysis, and governance alignment.

Unlike traditional tools that offer only surface-level tracking, the best data lineage solutions now enable field-level traceability, real-time impact analysis, and workflow-aware visualisation. Yet with so many vendors claiming end-to-end visibility, it’s easy to misjudge what’s truly actionable versus what’s cosmetic.

To cut through the noise, we’ve categorised each tool by how well it supports modern lineage use cases: discovering lineage across complex pipelines, driving change impact analysis, improving data quality, and strengthening regulatory compliance. Whether you're scaling an enterprise data stack or modernising analytics in a startup, this guide will help you shortlist the right solution.

Top 25 data lineage tools in 2025

We’ve grouped the tools into three practical categories:

Legacy Enterprise Data Lineage Tools

Legacy enterprise lineage tools have long supported complex, compliance-heavy organisations. They offer deep integration across traditional databases, ETL systems, and data warehouses, often with powerful audit trails, role-based access, and policy enforcement. While they may come with higher costs and more involved setup cycles, these platforms are trusted for their robustness, governance maturity, and enterprise readiness.

Tools under consideration:

1. Informatica Enterprise Data Catalog

2. Collibra Data Intelligence Platform

3. IBM Watson Knowledge Catalog

4. SAP Data Intelligence

5. Microsoft Purvie

6. Alation

7. Oracle Enterprise Metadata Management

8. BigID

9. Erwin Data Intelligence by Quest

10. Solidatus

1. Informatica Enterprise Data Catalog / Data Lineage

Informatica’s lineage is part of its broader EDC and Axon platform, offering automated metadata scanning, impact analysis, and governance workflows.

Key Strengths:

1. Covers ETL, cloud, mainframe, BI and data lakes

2. Supports detailed impact analysis and audit reporting

3. Integrated with Informatica’s larger governance stack and AI-based classification

Ideal For: Enterprises with very large, multi-system environments requiring thorough lineage and compliance

Limitations: UI and transformation modelling can feel dated; steep learning curve; performance slowdowns reported by developers 

2. Collibra Data Lineage

A feature within the Collibra Data Intelligence Platform, offering visual column‑level lineage across modern data environments.
Key Strengths:

1. Visual mapping from sources to analytics using QueryFlow

2. Integrates policy, glossary and trust scores into lineage views

3. Well‑rated governance and collaboration features

Ideal For: Organisations needing enterprise-grade governance, compliance documentation and oversight

Limitations: High implementation complexity and cost; connector support gaps (e.g. PySpark, AWS Glue); user feedback cites occasional bugs and support delays 

3. Manta

Engineering‑focused lineage tool by IBM offers deep code-level lineage across databases, SQL, ETL and custom scripts.

Key Strengths:

1. Parses code and pipelines automatically across 50+ technologies
2. Produces precision lineage including transformation logic
3. API‑first design enables observability system integrations

Ideal For: Tech‑heavy data engineering teams needing reliable, high‑fidelity lineage

Limitations: Interface is technical and less accessible for business audiences; not designed for governance-first workflows 

4. Microsoft Purview

Purview is an Azure‑native governance platform with integrated lineage for Power BI, Synapse, Fabric and SQL services.

Key Strengths:

1. Automatic lineage capture in Microsoft ecosystems

2. Role-based access control and policy enforcement

3. Hybrid support and integration with M365 and cloud services

Ideal For: Microsoft‑centric enterprises using Azure and Power BI as core platforms

Limitations: User experience feels basic; limited lineage support outside Microsoft data stack; fewer connectors for open-source tools 

5. IBM Watson Knowledge Catalog

Part of IBM Cloud Pack for Data, this tool embeds data lineage within an AI-first metadata and governance platform.

Key Strengths:

1. Supports model lineage and AI pipeline governance

2. Built‑in data quality scoring and profiling

3. Seamless integration with IBM tools (e.g. DataStage, Cognos)

Ideal For: Organisations using IBM’s ecosystem with AI governance needs

Limitations: Interface and usability lag behind modern tools; limited appeal for non-IBM environments.

6. SAP Data Intelligence

SAP Data Intelligence  combines lineage tracking, orchestration and metadata discovery in SAP-heavy landscapes.

Key Strengths:

1. Automated lineage plus MLOps and pipeline orchestration

2. Deep integration across SAP modules and external data sources

Ideal For: Organisations with heavy SAP investment seeking end-to‑end lineage in data and ML pipelines

Limitations: Configuring cross-platform linkage often complex; less maturity outside SAP ecosystem 

7. Oracle Enterprise Metadata Management (Lineage)

Oracle’s  metadata suite offers lineage and impact analysis across Oracle Data flows and platforms.
Key Strengths:

1. Tight lineage tracking in Oracle BI, ETL and databases

2. Change tracking and version control

Ideal For: Enterprises standardised on Oracle tech, needing governance aligned with Oracle Cloud infrastructure

Limitations: Weak integration beyond Oracle products; features often limited to Oracle ecosystems

8. Alation Data Lineage

Part of Alation’s catalog and governance stack, lineage is enriched with usage metadata and trust scoring.

Key Strengths:

1. Automatic lineage ingestion from BI tools, SQL and dbt pipelines

2. Trust-flags and lineage freshness signals

3. Collaborative workflows and glossary capabilities

Ideal For: Organisations balancing discovery, governance and self-serve analytics

Limitations: Requires manual setup for less common pipelines; may feel heavyweight relative to newer agile tools 

9. Erwin Data Intelligence (Quest)

Known for enterprise metadata modelling and lineage visualisation, Erwin is often paired with governance modules.

Key Strengths:

1. Works across diverse enterprise systems, from legacy to cloud

2. Strong model-centric lineage views and audit features

Ideal For: Teams with rigorous modelling requirements and hybrid infrastructure

Limitations: Often implemented via professional services; slower pace of UI evolution 

10. Solidatus

Solidatus
is built as a lineage‑first platform with visual mapping, version control and integrated governance analytics.

Key Strengths:

1. Automatic fine‑grain lineage over complex system landscapes

2. Visual business‑process mapping and transformation tracking

3. Strong in regulated environments; recognised in 2025 Gartner MQ for governance platforms

Ideal For: Large organisations needing clear, auditable data-life-cycle tracing across systems

Limitations: Higher price tier; steep learning curve; visual complexity may challenge new users 

Mid-Market Data Lineage Tools

Mid-market lineage platforms are built for agility. These tools offer fast deployment, intuitive interfaces, and powerful automation features tailored to hybrid and modern data stacks. Ideal for growing organisations or decentralised teams, they combine flexibility with strong metadata coverage, often with a focus on embedded lineage, column-level tracking, and ease of collaboration across data roles.

Tools under consideration:

1. OvalEdge

2. Atlan

3. Secoda

4. Select Star

5. Castor

6. Zeenea

7. Metaphor

8. Alteryx Connect

9. Unifi Data Catalog

10. Stemma

11. OvalEdge

OvalEdge is an end-to-end data governance and data catalog platform with data lineage embedded at its core. Lineage is not an add-on but is automatically generated and tightly integrated across metadata, glossary, quality rules, and access controls.

Key Strengths:

1. Automatically builds column-level lineage by parsing SQL, PL/SQL, ETL scripts, BI reports, and data models across 150+ connectors
   

2. Captures lineage across databases, data lakes, ETL tools, SaaS apps, spreadsheets, and BI platforms in one unified view
   

3. Provides impact analysis and dependency tracing for any object (tables, columns, reports, files, glossary terms, and policies, etc.)
   

4. Links business terms directly to technical assets in the lineage graph, enabling semantic tracing and role-based access
   

5. Supports both row and column level lineage, including versioned snapshots of lineage changes over time
   

6. Designed for cross-functional use with self-service exploration for business users and deep technical views for data teams

Ideal For: Organisations seeking a single platform for data governance, cataloging, and automated lineage, usable by both business and technical teams.

12. Atlan

Atlan is a modern metadata workspace with built‑in lineage, cataloging, governance and collaboration tools.

Key Strengths:

1. Intuitive UI that works across data engineers, analysts and stewards

2. Supports automated lineage for dbt, Snowflake, Airflow, Fivetran 

3. Integrates with Slack, Teams, Jira and Chrome extension for embedded context

Ideal For: Modern teams seeking active metadata, collaboration, and data governance with minimal friction

Limitations: High licensing costs unless negotiated; complex permissions model; occasional bugs during rapid feature rollouts 

13. Secoda

Secoda is a self-service data knowledge and lineage platform aimed at ease of use for technical and non-technical teams.
Key Strengths:

1. Combines lineage, search, documentation and access requests in one tool

2. Native integration with dbt and Fivetran; built-in AI chatbot for data queries

3. Excellent onboarding speed for small to mid‑sized teams

Ideal For: Organisations seeking lightweight lineage with context and documentation capabilities

Limitations: Limited governance functionality compared to heavier platforms; occasional bugs reported

14. Select Star

Select Star offers metadata, lineage, and usage tracking focused on dbt and BI environments.

Key Strengths:

1. Strong column-level lineage visualisation for dbt, Snowflake, Looker and other analytics stacks 

2. Popularity analytics and sidebar Q&A features help adoption among analysts

3. Lightweight, cost-effective deployment model

Ideal For: Analytics-focused teams wanting visibility on transformations and query dependencies 

Limitations: Governance workflows such as steward approvals or glossaries are not core features 

15. Castor (formerly Coalesce Catalog)

Castor delivers column-level lineage and rich metadata browsing for analytics environments.

Key Strengths:

1. Tracks lineage from staging layers through BI tools like Looker and Power BI
2. Popularity scoring helps surface critical assets and support deprecation planning
3. Responsive support and continuous enhancements

Ideal For: Mid-market teams prioritising intuitive lineage visualisation and discovery

Limitations: Does not trace derived column dependencies; lineage may miss certain edge transformations 

16. Zeenea

Zeenea is a modern metadata platform with integrated lineage visualisation and discovery.

Key Strengths:

1. Offers visual maps of lineage with metadata tagging and asset discovery
   

2. Targets cloud-native stacks with user-friendly interface for data teams

Ideal For: Cloud-first teams needing simple lineage alongside metadata discovery

Limitations: Less mature governance features and smaller integration ecosystem than heavyweight platforms 

17. Metaphor

Visual-first lineage and discovery platform created by lineage experts from Airbnb.

Key Strengths:

1. Graph-based lineage showing flows, usage patterns and ownership context

2. Collaboration features and easier access for business users via Slack/Teams

Ideal For: Organisations focused on traceability and transparency in data pipelines

Limitations: Still evolving in governance depth and automated compliance features 

18. Alteryx Connect

Part of the Alteryx platform, Connect brings lineage tracking to data prep and analytics pipelines.

Key Strengths:

1. Lineage tied directly to Alteryx workflows, preparation pipelines and dashboards

2. Easy glossary and collaboration features for analysts

Ideal For: Teams already using Alteryx Designer for self‑service analytics

Limitations: Limited lineage beyond Alteryx ecosystem; performance issues reported in large deployments 

19. Unifi Data Catalog

Unifi offers metadata enrichment and lineage mapping through ML-powered suggestions and user collaboration.

Key Strengths:

1. Automated cataloguing with machine recommendation features

2. Self-service data discovery and visual lineage insights

Ideal For: Organisations looking for smart metadata management and easy adoption

Limitations: Platform maturity and roadmap clarity vary; premium features may require extra cost

20. Stemma

Built on top of Amundsen, Stemma offers managed lineage and analytics intelligence for teams.

Key Strengths:

1. Extends open-source lineage with a managed interface and additional intelligence

2. Supports metadata ingestion and basic governance features for modern stacks

Ideal For: Growing organisations wanting Amundsen lineage without self-hosting complexity

Limitations: Still emerging in lineage depth and governance scope compared to proprietary counterparts

Open-Source Data Lineage Tools

Open-source data lineage tools provide flexible, extensible frameworks for metadata and lineage tracking. They’re ideal for engineering-first teams looking to build custom workflows, automate metadata extraction, and extend lineage across complex stacks. While these tools may require more setup and technical investment, they offer unmatched control and community-led innovation.

Tools under consideration:

1. OpenLineage

2. Marquez

3. DataHub

4. OpenMetadata

5. Apache Atlas

21. OpenLineage

OpenLineage is an open standard and framework for lineage collection, designed to consistently capture metadata about jobs, datasets, runs, and pipelines across tools.

Key Strengths:

1. Provides a vendor-neutral lineage spec widely adopted across E2E pipelines

2. Integrates easily with tools like Airflow, dbt, Spark, Kafka via libraries and consumers

3. Enables interoperable lineage capture from diverse execution engines and schedulers

Ideal For: Engineering-driven teams seeking standardised lineage across polyglot orchestration and analytics stacks

Limitations: Requires pairing with a metadata store (like Marquez); full lineage is dependent on client or pipeline integration.

22. Marquez

Marquez is the reference metadata service and UI for OpenLineage, capturing, storing, and visualising execution and lineage data.

Key Strengths:

1. Real-time lineage ingestion, capturing run/job/dataset metadata via OpenLineage APIs 

2. Provides basic visualisation and API access for building lineage dashboards or integrations

3. Lightweight deployment, highly extensible and suited to observability workflows

Ideal For: Teams looking for a self-hosted, open-source lineage backend and UI to support OpenLineage event ingestion

Limitations: UI is basic; documentation and deployment require engineering support; manual integration often needed 

23. DataHub

Created by LinkedIn and now community-driven, DataHub is a metadata platform supporting fine-grained table and column lineage, access controls, and dataset observability.

Key Strengths:

1. Automatically infers lineage from SQL and ingestion pipelines using Python SDK and built-in parsers 

2. Offers column-level and table-level lineage, visual graph explorer and downstream impact tracing

3. Rich API and metadata model support for dashboards, charts, jobs, and datasets

Ideal For: Engineering teams needing unified metadata and lineage, with scan-and-parse support of code-heavy SQL environments

Limitations: Initial setup and managing connectors can be complex; lineage may not capture all edge cases unless pipelines follow supported patterns 

24. OpenMetadata

OpenMetadata is a rapidly growing metadata platform with support for lineage tracking, governance workflows, and quality monitoring.

Key Strengths:

1. Full-column lineage ingestion via SQL parsers, dbt runs, Airflow/Prefect pipelines 

2. Lineage visualisation with manual editing UI, including drag-and-drop node relationships and query edge viewing
   

3. Integrated RBAC governance, glossary tagging, event notifications, and data profiling dashboards

Ideal For: Teams wanting a single platform for lineage, governance, data quality and discovery under one open-source framework

Limitations: Certain connectors (e.g. HDFS, procedural SQL lineage) may have gaps; manual adjustments sometimes needed for completeness 

25. Apache Atlas

Apache Atlas is a mature metadata and governance tool, especially strong within Hadoop-based ecosystems. It offers classification, data lineage, and policy management.

Key Strengths:

1. Enterprise-grade lineage tracking across Hive, Kafka, NiFi, Hadoop tools with policy enforcement and metadata model support

2. Supports lineage-based auditing, data classification and integration with governance frameworks in large-scale environments

3. Part of Apache ecosystem with scalable metadata lineage modeling

Ideal For: Organisations using Hadoop or big data platforms, needing deep lineage, compliance and integrated classification features

Limitations: UI and UX feel dated; deployment and configuration can be cumbersome; requires experienced engineering resources to extend tools beyond Hadoop 

Comparing the top 25 data lineage tools in 2025

While most data lineage tools provide core functionality such as flow visualisation and metadata capture, their depth and usability vary widely. Some are designed for enterprise governance, with granular policies and audit trails. Others focus on engineering visibility, automating lineage from code or pipelines. And some strike a balance between collaboration, automation, and control.

While nearly all tools provide basic flow visualisation, only a few go deep into automation, business linkage, or real-time lineage updates. This table highlights where each platform stands across 10 key capabilities.

**Disclaimer: The segmentations and capability evaluations presented above are based on our independent research and product documentation as of May 2025. Features, pricing, and positioning may evolve over time. We recommend speaking directly with individual vendors for the most up-to-date and tailored information.

Download the Data Lineage Tools Comparison Guide. Quickly find the right tool with feature breakdowns and use case fit.

Key Patterns Emerging from the Data Lineage Tool Landscape

Data lineage tools vary widely, not just in features, but in how well they support real-world use. Some focus narrowly on technical metadata; others offer broader capabilities but lack depth or automation. The best tools bridge technical and business needs, adapt to change, and make lineage useful for more than just compliance or documentation.

Whether you're building trust in KPIs, debugging broken reports, or assessing the impact of a change, the right tool should give you an accurate, end-to-end view of how data moves, transforms, and supports your business.

Here are five practical criteria to guide your selection:

1. Visual lineage is a baseline, but not a differentiator

Most tools now offer a visual interface for lineage, but this has become a standard expectation rather than a point of differentiation. The real value lies in the depth, tools that only offer table-level diagrams fall short when teams need column-level lineage for tracing issues or understanding data flow in detail.

2. Column-level lineage and impact analysis still divide the field

While some mid-market tools (like OvalEdge) offer strong support here, many others in the same category, and nearly all open-source tools, lack consistent column-level tracing and structured impact analysis. This weakens their ability to support change management, data debugging, or root cause workflows in complex environments.

3. AI and automation features remain loosely defined

Although many vendors claim to support AI or automation, in most cases this refers to automated metadata extraction or tagging. Very few offer advanced automation like auto-lineage from SQL parsing, anomaly detection, or AI-driven recommendations that actively reduce manual effort.

4. Lineage versioning is widely missing, even in enterprise tools

Versioning, or the ability to see how lineage has changed over time, is one of the least supported capabilities across the board. This is a concern for audit-readiness and for teams managing frequent schema or pipeline changes.

5. Glossary integration exists, but not always meaningfully linked

Most tools offer a business glossary, but only some integrate it well into the lineage view. Without this link, business users struggle to interpret technical metadata, and data governance remains siloed from day-to-day decision-making.

How to choose a data lineage tool

Choosing a data lineage tool isn’t just a matter of ticking off features, it’s about finding a solution that fits the scale, complexity, and maturity of your organisation’s data estate. A good tool should connect the technical flow of data with the business context in which it’s used, while being practical enough to keep pace with real-world changes.

Here are five essential factors to guide your evaluation:

1. Supports both technical and business lineage

Many tools focus purely on technical lineage, i.e., tracking tables, columns, and pipelines, but overlook the broader business context. A strong solution must also show how data supports business processes, reporting, and decision-making.

Ask yourself:
Can the tool trace a revenue metric not just to its source table, but also show who owns it, how it’s defined, and how it’s used across departments?

2. End-to-end lineage across systems and teams

Lineage must span beyond databases and warehouses to include spreadsheets, BI dashboards, APIs, and scripts. It should follow data from source to final report, including all transformations along the way.

Ask yourself:
Can the tool track data from a leasing system, through spreadsheet calculations, into Power BI dashboards, all in a single flow?

3. Automation that goes beyond metadata collection

Manual lineage quickly becomes outdated. While many tools claim AI or automation, few can reliably parse SQL logic, ETL code, or reporting definitions. Look for tools that can automatically generate and update lineage as systems evolve.

Ask yourself:
Will the tool detect a new join in your SQL pipeline or a changed calculation in Tableau, and update lineage accordingly?

4. Governance integration that makes lineage actionable

Lineage is most useful when linked to governance. The right tool should integrate ownership, glossary definitions, access controls, and quality rules, turning lineage from a visual diagram into a working part of your data governance strategy.

Ask yourself:
Can a data steward use lineage to understand policy violations or investigate a data quality alert?

5. Usability for both technical and non-technical users

Lineage should not be limited to data engineers. Analysts, governance leads, and business users must also be able to explore lineage with ease. Tools with intuitive UI, guided search, and clear navigation see far better adoption across teams.

Ask yourself:
Will a business analyst be able to navigate the lineage view without needing technical assistance?

Conclusion

Data lineage is no longer a “nice-to-have” but a foundational capability for any data-driven organization. Whether you're focused on ensuring regulatory compliance, accelerating troubleshooting, or empowering business users with trusted data, lineage provides the necessary transparency into where data comes from, how it's transformed, and how it’s used.

As we've seen, both Business Data Lineage and Technical Data Lineage play essential roles, one providing business context, the other technical clarity. Together, they form a complete picture of your data ecosystem.

The key to unlocking this value lies in choosing the right approach, supported by best practices and the right tools. By investing in a solution that automates lineage capture, integrates with your broader governance framework, and serves both technical and business teams, you set the foundation for more informed decisions, faster response to issues, and stronger trust in your data.

Frequently Asked Questions