10 Data Lineage Tools for 2027, Compared

Samuel Nagy

VP of Strategic Growth

November 4, 2026

Data lineage used to be a diagram for the data team. Going into 2027 it is evidence: for auditors asking where a reported number came from, for regulators asking which data trained an AI system, and for agents that need to cite a source before they act. This guide compares ten lineage tools on what decides the choice: how deep the lineage goes, how far it reaches across platforms, and whether the business can read it.

TL;DR

Lineage tools differ on three things: depth (system, table, column, down to the report visual), reach (one platform or every platform a number passes through) and audience (engineers only, or business users and auditors too). Platform-native lineage in Databricks, Snowflake and Purview is good inside its platform and stops at the edge. Dawiso leads for organizations that need lineage across platforms down to the Power BI visual, in the same product as the catalog, glossary and owners, served to AI agents over MCP.

What Data Lineage Tools Do

A data lineage tool shows where data comes from, how it is transformed and where it goes. It follows a value from a source system through pipelines and transformations into a warehouse, a semantic model and finally a report, a dashboard or an AI system. Teams use it for three jobs: impact analysis before a change, root-cause analysis when a number is wrong, and evidence for auditors and regulators.

Lineage comes at different levels of detail. System and table lineage shows which datasets feed which. Column-level lineage traces individual fields through transformations. Visual-level lineage goes one step further into BI and shows which chart on which report page uses which measure. The way a tool captures lineage matters as much as the depth: from the platform's own metadata, from transformation frameworks like dbt, from SQL parsing or from runtime events. We explain the methods in data lineage techniques.

Why Lineage Matters More in 2027

AI regulation asks lineage questions. Colorado's law on automated decision-making technology, SB 26-189, requires developers from 1 January 2027 to document the categories of data a system was trained on, and lets consumers ask for the personal data a system used. California's CCPA regulations on automated decision-making technology also apply from 1 January 2027. Public companies have long had to show auditors where the numbers in their filings come from under SOX, and US banks have to show that the data feeding their models is suitable under SR 11-7; global banks answer the same question under BCBS 239, and the EU AI Act adds data governance duties for high-risk AI from 2 December 2027. All of them come down to one capability: show which data fed this output.

Agents need to cite their sources. On 20 October 2026 Gartner listed AI context platforms among its top strategic technology trends for 2027. Lineage is a core part of that context. An agent that can say a revenue figure comes from a specific Snowflake table through a specific dbt model, owned by Finance, is one a controller can trust.

The platforms improved inside their walls. In June 2026 Databricks made external lineage in Unity Catalog generally available, and Snowflake opened an OpenLineage API in public preview so pipelines can report lineage into Horizon. Each improvement makes lineage better inside one platform, and each one stores it there.

Lineage specialists were bought. IBM acquired Manta in 2023 and folded it into watsonx.data intelligence. Cloudera agreed to acquire Octopai in November 2024. Two of the best-known independent lineage products now follow larger platform roadmaps.

Where Lineage Usually Stops

Most lineage breaks at a boundary: where data leaves one platform for another, or where it enters the BI tool. A typical flow runs from an ERP or CRM through ingestion into Snowflake or Databricks, through dbt, into a Power BI semantic model and onto a report page. Each platform sees its own segment well. The question a controller asks, "where does this chart come from?", crosses all of them.

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The 10 Tools at a Glance

The table summarizes how far each tool's lineage reaches and who it is built for, based on what each vendor documents.

Tool	Lineage reach	BI depth	Built for
Dawiso	Across warehouses, dbt and BI, in one catalog	Down to the Power BI visual	Business and technical users together
Collibra	Technical lineage as a suite module	Report level	Governance offices
IBM (Manta)	Code-level scanners across many technologies	Report level	IBM customers, technical teams
Informatica CDGC	Informatica scanners, strongest with Informatica pipelines	Report level	Informatica and Salesforce estates
Solidatus	Modeled lineage, including manual and regulatory flows	Modeled	Regulatory reporting in banks
Atlan	Modern data stack connectors	Report level	Engineering-led teams
Microsoft Purview	Microsoft estate, item level for most Fabric items	Power BI artifacts	Microsoft-centric organizations
Databricks Unity Catalog	Inside Databricks, external lineage GA	Dashboards in Databricks	Lakehouse teams
Snowflake Horizon	Inside Snowflake, OpenLineage API in preview	Via external connectors (preview)	Snowflake-first teams
Cloudera (Octopai)	Automated cross-system lineage	Report level	Cloudera customers

1. Dawiso

The leader for cross-platform lineage that the whole organization can read. Dawiso leads this list because it answers the question auditors, controllers and AI agents actually ask, where a number on a report comes from, across every platform the number passes through and in a view a business user can follow. Its Interactive Data Lineage follows a value from Snowflake or Databricks through dbt into the Power BI semantic model and down to the individual visual on a report page: open a measure and see every chart that shows it, or open a chart and see every measure, column, table and upstream source behind it.

Lineage is built from the metadata each platform exposes, with column-level lineage from transformation frameworks such as dbt and a SQL parser program for further column-level coverage. You expand any node from a business overview to column detail, follow flows upstream and downstream, annotate them and export the expanded tree. Data tagged as sensitive, for example under GDPR, is flagged automatically when it flows into another table.

What sets Dawiso apart from lineage-only tools is that the lineage lives in the same product as the catalog, the business glossary and the owners. Every node carries its definition, owner and sensitivity, and the MCP Server serves the same lineage to AI agents with the user's permissions, so an agent can cite where a number came from. Metadata is scanned read-only and copies no rows.

Lineage that stops at the warehouse explains your pipeline. Lineage that reaches the report visual explains your numbers.

See it in action

Dawiso Interactive Data Lineage

Trace data from source to the report visual, across the platforms you run.

2. Collibra

Lineage as a module of a governance suite. Collibra offers technical lineage as part of its governance platform, harvested from databases, ETL tools and BI. It suits organizations that already run Collibra for governance. Lineage is licensed and rolled out as one more module, and configuring scanners across a large estate is typically a services project.

3. IBM watsonx.data intelligence (Manta)

Code-level lineage inside the IBM stack. IBM acquired Manta, a well-known independent lineage scanner, in 2023, and its lineage is now part of watsonx.data intelligence, formerly IBM Knowledge Catalog. It parses code across many databases and ETL technologies. Since the acquisition, its roadmap and packaging follow IBM's data and AI platform, which matters if your estate is not built on IBM.

4. Informatica Cloud Data Governance and Catalog

Lineage for Informatica-centric estates. Informatica's CDGC builds lineage through its scanners and is at its most detailed where Informatica runs the pipelines. Salesforce completed its acquisition of Informatica in November 2025, and the 2026 releases concentrate on Salesforce Data 360 and MuleSoft Agent Fabric.

5. Solidatus

Modeled lineage for regulatory reporting. Solidatus is a lineage modeling platform used mostly by banks for regulatory reporting, with bi-temporal version control so you can show how lineage looked at a past date. Its July 2026 release added MCP support and AI-assisted mapping with human review. Lineage is often modeled rather than scanned, which suits regulatory flows that cross manual steps and takes ongoing effort to keep current.

6. Atlan

Lineage for the modern data stack. Atlan builds automated lineage from connectors to Snowflake, Databricks, dbt and BI tools as part of its catalog. It is aimed at engineering-led teams. Expect a higher entry price than Dawiso. See Dawiso vs Atlan.

7. Microsoft Purview

Lineage inside the Microsoft estate. Purview scans lineage from Azure services, Fabric and Power BI into its Unified Catalog. According to Microsoft's documentation, for Fabric items other than Power BI only item-level lineage is scanned, sub-item lineage for Lakehouse tables is not supported, and cross-workspace lineage for non-Power BI items is not supported. Lineage across Snowflake, Databricks or SAP is thinner. See Dawiso as a Purview alternative.

8. Databricks Unity Catalog

Lineage inside the lakehouse. Unity Catalog captures table and column lineage automatically for workloads that run in Databricks, from source tables to notebooks, models and dashboards. In June 2026 Databricks made external lineage generally available, so outside systems can be added to the graph. The lineage lives in Databricks; Dawiso scans it and connects it to the rest of the estate, as in context layer for Databricks.

9. Snowflake Horizon

Lineage inside Snowflake. Horizon Catalog shows lineage for objects in Snowflake, and in 2026 Snowflake added an OpenLineage API in public preview so tools such as Apache Airflow can report lineage into Horizon, plus metadata connectors for external sources in preview. As with Databricks, the result is lineage governed inside Snowflake. Dawiso scans Snowflake, including Cortex agents and semantic views, into cross-platform lineage, as described in governing Snowflake Cortex agents.

10. Cloudera (Octopai)

Automated lineage now inside Cloudera. Octopai built automated cross-system lineage for BI-heavy estates. Cloudera agreed to acquire it in November 2024 and has folded it into its own metadata management, so its future follows Cloudera's hybrid data platform rather than a standalone lineage roadmap.

Open Source and OpenLineage

OpenLineage is an open standard for collecting lineage events from running jobs, a graduated project of the LF AI & Data Foundation, with Marquez as its reference implementation. Airflow, Spark and dbt integrations emit OpenLineage events, and platforms such as Snowflake now accept them. Open-source catalogs like DataHub and OpenMetadata also draw lineage graphs. They suit engineering teams willing to run and extend the stack themselves; business-facing views, ownership and governance workflows are usually built or bought on top. See Dawiso vs DataHub and Dawiso vs OpenMetadata.

How to Choose

Pick one number your organization argues about, such as gross margin on the board report, and ask each tool to trace it end to end. These questions separate the field quickly.

Question	Why it matters
Does it trace across every platform the number passes through?	Platform-native lineage stops at the platform edge.
Does it reach the report visual, or stop at the dataset?	Impact analysis needs to know which chart changes, not just which report.
Where does column-level lineage come from for each source?	Coverage depends on dbt, SQL parsing or the platform; ask per source.
Can a business user and an auditor read it?	Lineage only engineers can read does not answer audit questions.
Does each node show its owner, definition and sensitivity?	Lineage without context shows flows but not responsibility.
Can AI agents read it with the user's permissions?	Agents need to cite sources; MCP access makes that possible.
Can it produce evidence for SOX, SR 11-7 or BCBS 239 reviews?	Auditors ask for the path of a specific reported number, at a specific date.
Who owns the vendor?	Manta and Octopai now follow larger platform roadmaps.

Where Dawiso Fits

Dawiso is the lineage tool for organizations whose numbers cross platforms and whose questions come from the business. It traces data from source systems through Snowflake, Databricks and dbt into Power BI down to the visual, keeps owners, definitions and sensitivity on every node, and serves the same lineage to AI agents over MCP. It is independent of the platforms it traces, so the lineage you build does not depend on staying with any one of them. For the wider governance market, see 10 data governance tools for 2027.

FAQ

What are the best data lineage tools for 2027?

Dawiso leads for organizations that need lineage across several platforms down to the report visual, in one product with the catalog and glossary. Other tools often evaluated are Collibra, IBM watsonx.data intelligence with Manta lineage, Informatica Cloud Data Governance and Catalog, Solidatus, Atlan, Microsoft Purview, Databricks Unity Catalog, Snowflake Horizon and Cloudera with Octopai, plus open-source options built on OpenLineage. They differ in how deep the lineage goes, how far across platforms it reaches, and who can read it.

What does a data lineage tool do?

A data lineage tool shows where data comes from, how it is transformed and where it goes, from source systems through pipelines and warehouses to reports and AI systems. It is used for impact analysis before a change, root-cause analysis when a number is wrong, and evidence for auditors and regulators. Good tools capture lineage automatically and show it at the level of detail each user needs.

What is the difference between data lineage and data provenance?

Data lineage describes the path data takes: which systems, transformations and reports it flows through. Data provenance describes the origin and history of a specific piece of data: where it was created, by whom, and what happened to it. Lineage answers how data moves; provenance answers where a particular value came from and whether it can be trusted. Most lineage tools support provenance questions by keeping history and ownership alongside the flow.

What is column-level lineage?

Column-level lineage traces individual columns rather than whole tables: which source columns feed a target column and through which transformation. It is what makes impact analysis precise, because renaming one column affects specific downstream fields and reports, not everything that touches the table. Coverage depends on the source; tools capture it from transformation frameworks such as dbt, from SQL parsing, or from the platform itself.

What is a data lineage diagram?

A data lineage diagram is the visual graph a lineage tool draws: nodes for systems, tables, columns and reports, and edges for the flows between them. A useful diagram lets you expand a node to more detail, follow flows both upstream and downstream, and see ownership and sensitivity on each node rather than a static picture.

Why do AI systems need data lineage?

Because regulators and users ask where an AI answer came from. Colorado SB 26-189 requires developers to document the categories of data a system was trained on from 1 January 2027, California's automated decision-making rules apply from the same date, and the EU AI Act asks for data governance on high-risk systems from 2 December 2027. Lineage is how you show which data feeds which AI system, and it lets an agent cite the source and owner of a number instead of guessing.