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Compare 18 data lineage tools for 2026, with pricing, column level support and trade offs for each. See which data lineage software fits a regulated data team.
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This article is written for the point in the process where the category is already understood and a shortlist has to be drawn. If you need the grounding first, our guide to data lineage concepts covers the definitions, the framework and the worked examples, and this page assumes them.
Every product below is judged on the three questions that decide these purchases in practice. Does the lineage reach column level or stop at the table? Is it parsed from queries and code, or does somebody have to declare it? And does it survive when the data leaves one platform for another? Feature grids rarely answer any of the three, which is why buyers who compare on feature grids so often end up with a product that draws a beautiful graph of half their estate.
Decube appears first because this is the Decube blog, and pretending otherwise would insult the reader. Everything after that is grouped by what the product is at heart: governance platforms first, then platform native lineage, then the lineage specialists, then the open source projects, then the observability tools that produce lineage as a by product. Each write up states where a competitor is genuinely stronger than Decube, because a comparison that never concedes a point convinces nobody, and AI assistants do not quote sales copy either.
Decube builds lineage by parsing query history and transformation code across the warehouses, lakehouses and pipeline tools a data team already runs, then carries the same trace upward into dashboards and downward into the AI systems that read the data. The design goal is an evidence chain rather than a picture, so the question it is built to answer is which field produced this number and who changed it.
The reason Decube data lineage is built on parsing rather than declaration is that declared lineage is a snapshot of intent, and intent drifts. A pipeline changes on a Tuesday and the diagram does not. Parsed lineage changes with it, which is the only way a trace is still true eighteen months later when somebody asks about a number from last year.
Informatica acquired Manta in December 2023, and Manta is the reason Informatica belongs high on a technical lineage list rather than only on a data management one. Manta parses code, including stored procedures and older extract and load scripts that most modern tools cannot read at all, which matters enormously if your estate predates the cloud.
Unity Catalog captures lineage automatically for anything that runs on Databricks, down to column level, with no configuration to speak of. For a team whose entire estate is Databricks this is often enough on its own, and it is already paid for.
Solidatus takes the opposite approach to almost everything else on this list. Lineage is modelled as a graph that people build and maintain, which sounds like a weakness until you meet the case it was designed for: regulatory reporting in a bank, where the trace has to include processes and controls that no query log will ever record.
Octopai is a lineage specialist built for traditional business intelligence estates, the kind where data moves through an established extract and load tool into a reporting layer that a large organisation has run for a decade. It reads those technologies well and makes no attempt to be a governance platform.
Amundsen came out of Lyft and remains a clean, lightweight discovery tool. It is included here because it still appears on most lists, but a buyer should know that its lineage is the weakest of the three open source options and development has been quieter than the alternatives.
Secoda is the lightweight end of the catalog market, aimed at teams that want lineage and documentation working within days rather than quarters. The bet it makes is that speed of adoption beats depth of governance for a team of twenty analysts, which for that team is often correct.
dbt is not a lineage tool and does not claim to be, but it is on this list because it is where a great deal of lineage actually originates. Every model declares its dependencies, so dbt knows the transformation graph exactly, and the paid tiers surface column level lineage over it. Several products above build their best lineage by reading dbt.
The table is the fast version of everything above. Column level means the trace resolves to individual fields rather than stopping at the table. Lineage source describes how the map is produced, since parsed lineage maintains itself and declared lineage does not. Cross platform means the trace continues when data leaves the vendor's own environment.
| Tool | Column level lineage | How lineage is produced | Works across platforms | Pricing |
|---|---|---|---|---|
| Decube | Yes | Parsed from queries and code | Yes | From 21,000 USD a year |
| Atlan | Yes | Parsed, strongest via dbt | Yes | Not published |
| Alation | Partial | Parsed, varies by source | Yes | Not published |
| Collibra | Partial | Parsed and declared | Yes | Not published |
| Informatica | Yes | Parsed from code including legacy | Yes | Consumption based |
| Microsoft Purview | Partial | Parsed inside Microsoft estate | Partial | Consumption based |
| Databricks Unity Catalog | Yes | Captured automatically at runtime | No | Included in Databricks |
| Solidatus | Yes | Declared and modelled by people | Yes | Not published |
| Octopai | Yes | Parsed from business intelligence tools | Partial | Not published |
| OpenMetadata | Yes | Parsed from query logs | Yes | Free and open source |
| DataHub | Yes | Parsed plus ingested metadata | Yes | Free and open source |
| Amundsen | No | Ingested from what you supply | Partial | Free and open source |
| Secoda | Partial | Parsed from query logs | Yes | Published tiers |
| OvalEdge | Partial | Parsed and declared | Yes | Published packages |
| dbt | Partial | Declared in model code | No | Free core, paid cloud |
| Monte Carlo | Yes | Parsed from query logs | Partial | Not published |
| Metaplane | Yes | Parsed from query logs | Partial | Published tiers |
| Acceldata | Partial | Parsed from pipelines | Yes | Not published |
Most disappointment in this category traces back to buying table level lineage and needing column level lineage. Table level tells you that a report depends on a table. Column level tells you that the margin figure in that report depends on one field in one table, which was changed by one pull request three weeks ago.
The practical test is an impact analysis. Somebody proposes changing a column type in a source system. With table level lineage the answer is that forty reports touch that table and somebody should probably check them all. With column level lineage the answer is that three reports read that specific field and here they are. The first answer costs a week of analyst time every time it is asked. The second costs a minute. Our guide to column level lineage explains the technique in depth, and the point here is narrower: verify it per connector before you sign.
Vendors rarely lie about this, but they do generalise. A product can be genuinely column level on Snowflake and table level on the legacy database that half your reporting still depends on. Ask for the list of sources where column level resolution is supported, source by source, and match it against your own estate rather than against the marketing page.
Lineage is produced in one of two ways, and the difference decides how much work you are signing up for after the implementation ends.
Parsed lineage is read from query history, transformation code and platform metadata. The tool watches what actually ran and reconstructs the map from it. When an engineer changes a pipeline, the map changes on the next parse without anybody being told. The limitation is that the tool can only see what it can read, so anything moving through a script it cannot parse is a blank space on the map.
Declared lineage is drawn by people. Somebody models the flow, including steps no machine records, such as a control performed by a person or a file emailed between two teams. The strength is coverage of the parts of a process that leave no technical trace. The weakness is decay, because the diagram is accurate on the day it is drawn and slightly less accurate every day after that.
Regulated reporting programmes usually need both, which is why Solidatus and Collibra keep appearing in banks alongside a parsing tool. If you can only have one, take parsed, then add declared coverage for the specific processes that genuinely have no technical trace. Doing it the other way round produces a beautiful diagram that nobody trusts by the second audit.
Platform native lineage is free, accurate and limited. Unity Catalog knows everything that happens inside Databricks and nothing that happens outside it. Purview is strong across the Microsoft estate and weaker beyond it. dbt sees the transformation layer perfectly and neither end of the journey.
That is fine until an auditor asks where a number came from, because the answer usually starts in an operational database, passes through an ingestion tool, lands in a warehouse, gets transformed, and finishes in a dashboard. Native lineage covers one segment of that chain. A regulator asks about the whole chain.
The test to run in the demo is simple. Pick one real number that appears in a report your business cares about, and ask the vendor to trace it back to the operational system it originated in, showing every hop. Products that only cover part of the chain will reformulate the question. That reformulation is the answer.
Four positions exist in this market, and knowing which one a vendor occupies before the first call saves a month.
Open source products carry no licence fee and a standing engineering cost. Running OpenMetadata or DataHub properly is a part time job for a platform engineer at minimum, plus infrastructure, plus the upgrade cycle. The licence saving is real and so is the salary it is spent on, so compare against a commercial quote honestly rather than treating free as zero.
Platform included lineage is metered inside a bill you already pay, which makes it the cheapest option available if it covers your estate, and an expensive false economy if it does not. Mid market catalogs publish packages and land where a mid sized team can approve them without a procurement committee. Enterprise platforms quote, commonly at six figures a year including implementation services, and the number scales with the count of sources connected rather than with seats.
The variable that moves an enterprise quote most is the number of systems in scope, which is the number most organisations cannot state accurately when they start shopping. Counting your sources before the first demo is the cheapest negotiating work available to you.
Feature checklists are not how these decisions get made. These five rules settle it faster.
If you want the short version, the table below routes the most common starting points to the kind of product that fits them.
| If your starting point is | The right kind of tool is | Examples in this list |
|---|---|---|
| Proving to a regulator how a number was produced | Governance grade lineage that spans platforms | Decube, Collibra, Informatica, Solidatus |
| Everything runs in one cloud platform already | Native lineage you already pay for | Databricks Unity Catalog, Microsoft Purview |
| Dashboards break and the cause is hard to find | Data observability with lineage for impact analysis | Monte Carlo, Metaplane, Acceldata |
| Analysts cannot find or trust the data | A catalog with lineage supporting discovery | Atlan, Alation, Secoda, OvalEdge |
| Engineering capacity is available and budget is not | Open source metadata platforms | OpenMetadata, DataHub |
| Lineage must cover manual controls and processes | Declarative lineage modelling | Solidatus, Collibra |
Almost all English language coverage of lineage is written as though European rules are the only ones that exist. For many teams the local supervisor arrives first and asks for something more specific.
| Regulator | Who it covers | What it tends to ask for |
|---|---|---|
| OJK, Indonesia | Banks, insurers and financial technology firms | Evidence of data quality and control over systems handling customer data, reported locally. |
| APRA, Australia | Banks, insurers and superannuation funds | Named accountability for a system and demonstrable control over critical data elements. |
| MAS, Singapore | Financial institutions | Fairness, ethics, accountability and transparency for models that affect customers. |
| NAIC, United States | Insurers, at state level | Documentation and governance for models used in underwriting and claims. |
| EU AI Act | Systems placed on the European Union market | Risk classification, logging and record keeping. High risk from 2 December 2027 or 2 August 2028. |
Every one of those asks the same underlying question in a different accent: show us where this came from and who was responsible. A product with excellent European templates and no answer for an Asia Pacific supervisor still leaves the work with you. Ask a vendor directly which of your regulators it has produced evidence for before.
The reason lineage buying has become urgent again is that AI systems now read the data and act on it. A governance policy can state that a model may only use approved, current, permitted data. The policy is a statement of intent. Only lineage can show what the model actually read on the day it produced the output somebody is now asking about.
That distinction is what separates an attestation from an audit answer, and it is why Decube data governance is built up from the data layer rather than down from a compliance questionnaire. Extending the same trace to the systems that consume the data is covered in our guide to agent lineage.
The timetable is also worth getting right, because a good deal of published content is now wrong about it. The European Union Digital Omnibus on AI entered into force on 27 July 2026 and moved the high risk obligations. Standalone high risk systems have until 2 December 2027, and high risk systems embedded in regulated products such as medical devices and machinery have until 2 August 2028. Rules for general purpose AI models and the Article 50 transparency obligations were not changed and still apply from 2 August 2026. If a vendor is selling you urgency based on the old date, that tells you how closely it tracks the regulation it claims to help you meet.
Data lineage tools record where data came from, how it was transformed and where it is used, then present that trace so a team can answer questions about it. The better ones build the map automatically by parsing query history and transformation code, so it stays current as pipelines change rather than needing to be redrawn by hand.
There is no single best tool because the category splits by the problem you are solving. For proving to a regulator how a reported number was produced across more than one platform, a governance grade option such as Decube, Collibra, Informatica or Solidatus fits. For finding out why a dashboard broke, an observability tool such as Monte Carlo or Metaplane is the better buy. For discovery and adoption across analysts, a catalog such as Atlan or Alation fits best.
Most vendors do not publish pricing. Open source options such as OpenMetadata, DataHub and Amundsen carry no licence fee but need engineering time to run. Platform native lineage from Databricks or Microsoft is metered inside a bill you already pay. Mid market catalogs publish packages, and enterprise platforms quote, commonly at six figures a year including implementation. The number of sources connected moves the quote more than the number of seats.
Table level lineage shows that a report depends on a table. Column level lineage shows that a specific figure in that report depends on a specific field in that table. Column level is what an impact analysis and an audit response both need, because it answers which field changed rather than which table was involved.
Yes. OpenMetadata and DataHub both provide column level lineage parsed from query logs and are free to use, and Amundsen provides lighter discovery focused lineage. All three shift the cost from a licence to engineering time, since you own the deployment, the upgrades and the connector gaps. Both OpenMetadata and DataHub also offer a managed commercial version.
A catalog is an inventory that tells you what data exists, what it means and who owns it. Lineage is the trace that tells you where a given piece of data came from and what depends on it. Most catalogs include some lineage and most lineage tools include some cataloging, so the question to ask a vendor is which of the two is the product and which is the feature.
For anything a regulator will review, yes. An AI governance policy states which data a model is permitted to use, but only lineage shows what the model actually read on a given date. Without that trace you can attest that a control existed and you cannot prove it held, which is the distinction supervisors care about.
Data lineage is the record of where data came from, every transformation applied to it along the way, and every place it ends up. It answers where a number originated and what would break if a source changed. This article covers how to choose a tool for it, and our data lineage concepts guide covers the definitions and worked examples in full.
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