Platform · Lineage

Lineage.understandData Lineage & Impact Intelligence

Lineage here is read from your actual SQL, ETL, and BI code, not from documentation that goes stale as the code changes. That's what makes 'where did this number come from' a one-click answer instead of a two-week project. And where a hop cannot be parsed, it lands as a declared claim rather than derived truth, and the gap shows: you always know which kind of hop you are looking at.

The product, at a glance

What Lineage is made of.

6 capabilities, and the part of the mechanism this product carries. Every box opens right here, with its full story one tap deeper.

tap any box, or any circle in the mechanism, and it answers right hereknow what matters

What changes

What changes when this is running.

Not promises of speed, but mechanisms: each of these happens because of how the product works, not because we say so.

Where did this number come from becomes a lookup, because lineage is parsed from the code that actually runs.

Releases stop being blind, because every downstream report and consumer is visible before a change ships.

Gaps stop hiding, because an unparsed hop shows as a declared claim, never silently merged into derived truth.

who lives hereData engineersReport ownersPlatform architectsInternal auditanswers toBCBS 239

The product

See every data flow. Understand every impact. Prove every step.

End-to-end visibility across the data lifecycle, from source to report. How data is transformed, who trusts it, and how a change lands on your business and regulatory outcomes.

Trust and Scale

Business outcomes

Complete end-to-end visibility

Accelerate impact analysis and change

Reduce incidents, errors and rework

Strengthen regulatory and audit evidence

Improve data quality and trust

Lower cost and operational risk

At a glance

Lineage captures and connects data movement and transformation from source to consumption, across your enterprise and your AI landscape.

Discover

Automatically discover lineage from systems, ETL, SQL, APIs and reports.

Map

Map technical, business and process lineage across the entire data value chain.

Analyse

Perform impact, root cause and what-if analysis instantly.

Prove

Provide audit-ready evidence with full lineage history.

Collaborate

Share, annotate and certify lineage with business context.

What it does

Lineage automatically captures, curates and visualises data lineage across all layers, technical, business, process and AI, so you can understand how data flows, where it is used, and the impact of change.

01 · DiscoverAuto-discover lineage from multiple sources: batch, streaming, real-time and APIs.

Hover, click or use the arrow keys to walk the lifecycle.

Lineage quality, scored

Lineage nobody can trust is a diagram. The score says how much of the estate is actually traced, how accurate those traces are, and how current they are, because a lineage graph that is six months old is a picture of a system that no longer exists.

of 100 · Excellent

Trust by business process

Hover a dimension to see what it measures, or a cell to read the process behind it.

Busiest data flows, by usage

Account to deposit balance100
Deposit balance to LCR dashboard71
Deposit balance to FR 2052a68
Collateral value to ALCO report48
Deposit balance to liquidity metric47

Excellent · 85 to 100Adequate · 60 to 84Weak · 0 to 59

What the graph is made of

Column-level hops 46%Table and dataset hops 24%Report and metric edges 14%Reference lookups 10%AI and model edges 6%

An illustrative score for ABC Bank, the site's standing fictional institution. The shape is real; the values in your estate are your own.

The lineage explorer

One worked trace, from four source systems to four regulatory outputs. Lineage is only worth having at column level and across every hop, because the question is never 'where does this table come from' but 'which report moves if I change this column'.

L1 · SourcesRaw, captured faithfully

Core banking
Collateral system
General ledger
Market data feed

Where the data starts, and the only layer nobody governs.

An illustrative trace for ABC Bank. Solid is verified and active, and every hop carries the transformation that made it, not merely the fact that a hop occurred.

What one change would actually break

Impact analysis is the reason lineage exists. Direct impact is what a developer sees; total impact is what the institution feels, and the gap between those two numbers is the whole argument.

ImpactTypeDirect impactTotal impactStatus

Liquidity business rule: updatefail

type Logic · direct impact 1 · total impact 23

A rule change with ONE direct dependant and twenty-three total is the case that gets shipped on a Friday. The point of tracing at column level is that the twenty-three are known before, not after.

One mismatch, traced to its cause

What a root cause analysis actually looks like when the lineage is column level. The break is not where the symptom appeared; it never is.

01 · SymptomDeposit balance mismatchTraced automatically

A reconciliation break, noticed where the number is consumed rather than where it went wrong.

A chain is a conjunction. Take one link out and see what is left.

Traced automatically
AI-assisted
A person decides

The mismatch showed up in a regulatory schedule. The cause was three hops upstream, in a staging step missing a de-duplication rule. Without column-level lineage this is a week of meetings; with it, it is a query.

Key capabilities

End-to-end lineage

Source to report and API
Technical, business, process and reference lineage
Across on-premises, cloud, hybrid and SaaS

Impact and what-if analysis

Upstream, downstream and lateral impact
Column-level and field-level impact
What-if and scenario simulation

Lineage visualisation

Interactive graph view
Multi-level drill down and summarisation
Time travel and version comparison

AI, model and agent lineage

Model training data lineage
Feature and prompt lineage
Agent tool and output lineage

Data movement and transformations

ETL and ELT, SQL, code, APIs, streams, reports
Transformation logic and mappings
Data quality and rules along the flow

Lineage governance

Ownership and stewardship
Certification and trust indicators
Policies, classifications and usage tracking

Smart automation

AI-powered, and every one of them a recommendation rather than a decision.

Auto lineage discovery

Lineage inference and gap detection

Similar flow grouping

Transformation extraction

Change detection and alerts

Natural language explainer

Manual and AI-assisted

The same work, split. Everything on the left is something a person does; everything on the right is something the platform proposes, with evidence, for a person to accept. Nothing crosses the line on its own.

Manual

A person does this.

Define custom lineage
Annotate and add context
Validate and certify
Investigate complex flows
Approve and publish

AI-assisted

The platform proposes. A person accepts.

Auto-discover lineage
Infer missing relationships
Suggest business context
Recommend owners
Explain lineage in natural language

Lineage types covered

Technical

Systems, tables, columns
ETL, SQL and code
Streams and APIs

Business and process

Business lineage
Process lineage
Report lineage

AI and products

AI and ML lineage
Data product lineage
API and service lineage

Common use cases

Where institutions start

Assess impact of source or column changes
Root cause analysis for quality issues
Support regulatory reporting and audit requests
Understand data used in models and agents
Mergers, migrations and rationalisation
Retire data and unused assets safely

End-to-end workflow

01Discover lineage
02Capture and map
03Enrich and connect
04Analyse impact
05Certify
06Share and prove

What gets measured

Against your own baseline.

Critical CDEs with end-to-end lineageLineage coverageAverage impact analysis time savedChange failure rate reductionAudit and regulatory requests answered fasterLineage accuracy, precisionBusiness context coverageCertified lineage assets

Who it is for

Data engineers and architectsData stewards and ownersBusiness analystsApplication ownersRisk and compliance teamsAuditorsAI and ML practitioners

Integrations

OntaVera CatalogOntaVera QualityOntaVera GovernanceOntaVera PrivacyOntaVera Reference DataOntaVera Trust IntelligenceSnowflake, Databricks, BigQuery, RedshiftInformatica, Databricks Jobs, Talend, SSISTableau, Power BI, LookerAPIs and applicationsServiceNow, Jira, Confluence

Built on the OntaVera platform

Shared by every product in the suite.

Financial ontology and knowledge graph

Metadata fabric and connectors

AI and inference engine

Workflow and approvals

Security and access: RBAC, ABAC

Audit and evidence, immutable and encrypted

APIs and events: REST, GraphQL

Why OntaVera

Automatic and manual lineage together, for the highest accuracy
Complete visibility across data, AI and the business
Built-in governance, certification and auditability
Scalable, performant and enterprise grade
Contextual, actionable and easy to use

See every step.Understand every impact.Prove every answer.Change with confidence.

Inside Lineage6 sub-modules

When an examiner points at a number on a report, you need to show the exact columns that produced it. OntaVera traces every report line down to source columns, and it pairs that technical view with a business view your stewards and second line can read without an engineer in the room.

What you get

Trace any report line to the physical source columns behind it, transformation by transformation.

Two views sit on one graph: technical lineage for engineers, and business lineage in plain terms for stewards, second line, and auditors.

CDE lineage is derived from parsed code and scanned metadata, so the graph is generated from evidence and cannot be quietly edited.

Every lineage edge carries its evidence: the parsed statement, the connector scan, and the time it was read.

A hop with no evidence shows as unknown, and unknown counts as a finding rather than a pass.

Lineage sits on the same governed knowledge graph as your catalog, quality, and issues, which gives you one graph across all ten products.

How it works

Scan

Read-only connectors pull metadata from your warehouses, ETL tools, and BI layer. They read metadata only, copy nothing, and retain no data.

Derive

The platform parses the code and builds column-level edges. Lineage is derived from evidence and is never set by hand.

Map terms

Business terms and CDEs are linked onto the technical graph, so both views describe the same underlying edges.

Seal

Each lineage snapshot is sealed on the hash-chained ledger, so what you show at exam time is what the graph said that day.

Questions buyers ask

Try it yourself

Trace any number back to its source.

This is a pocket-size liquidity example. Click any node to trace it to source, or switch modes to see what a change would touch.

TRACE ANY NUMBER TO ITS SOURCE

report.lcr_daily · owner reg reporting · filed 07:00

traces to 4 sources · one hop declared, labeled apart

click any box · in the product this runs at column level across all your systems

Asked about Lineage

5 questions

It builds on it. Your declared lineage imports as a set of claims, and each claim is checked against lineage parsed from the code that actually runs. What holds is confirmed with evidence attached, and what does not hold opens as a tracked issue with a named owner instead of being quietly overwritten.

Reconciliation to source →

It needs read-only connections to your warehouses, ETL tools, and BI layer, and nothing more. The platform reads code and metadata only and copies no data, so nothing leaves the bank. If your security team requires it, BYOK and air-gapped deployment are both supported.

Security and deployment →

No, you do not need a mapping team. The graph is derived from parsed code and re-derives as the pipelines change, so nobody maintains it by hand. The day-to-day human work is judgment: stewards resolve the gaps the parser names on the graph and the discrepancies reconciliation flags as tracked issues with named owners.

SQL, ETL & BI parsing →

They get lineage read from production code rather than asserted, with the parsed statement behind every hop and any missing evidence shown as a named gap rather than a quiet hole. Each snapshot is sealed on the hash-chained ledger, so what you present at the exam is what the graph said on that date. Any walked path then exports with its evidence as a pack you can hand over.

Lineage explorer →

Agents work on top of the graph, for example proposing the owner notification list before a change ships, and a human decides who is told and who signs off. They never draw the lineage itself: every edge comes from parsed code and scanned metadata. Where evidence is missing, the graph shows unknown rather than letting anything guess an answer.

Impact & blast radius →

Stronger together

One shared record, so products compound.

Every product you add makes this one smarter, because they read and write the same governed record. Start where it hurts most; nothing you adopt later starts from zero.

Lineage + Quality

Add quality and every break arrives with the downstream reports already mapped from the same graph.

Lineage + Regulatory

Add evidence and any traced path exports as an exam-ready pack with proof at every hop.

Getting started

Start small, and prove it on your own systems.

Most banks start with one priority report and five source systems, live in 90 days, then bring in more of the platform behind exit gates they approve. Lineage is part of that first conversation.

Keep exploring

← Governanceavailable now Quality →available now The engines underneatharchitecture How decisions workframework

The next step

Start with proof, not a promise.

There are three ways in, and each one earns the next. You approve the exit criteria before we spend any more of your time or budget.

A 60-minute working session

Bring one report that is expensive to prove. We walk the mechanism against it. Nothing is required from your side.

A two-week guided sandbox

Your team works hands-on against a synthetic sample bank. It requires zero access to your systems and zero procurement.

A six-week proof on your systems

We use read-only metadata across five systems and one priority report, scored against criteria both sides sign first. The decision it produces is yours.

Book a working session See the adoption path