Announcing Data Lineage in Matia: See exactly how your data flows

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When a dashboard breaks or an AI model starts producing unexpected results, data teams face the same frustrating question: where did this go wrong? Without lineage, you're left playing detective, checking dbt models, querying warehouse logs, asking around to see who changed what. By the time you trace the issue back to its source, stakeholders are already losing trust.

The problem isn't just about fixing things faster. It's about understanding your data infrastructure well enough to make changes confidently. Before you deprecate a table, refactor a dbt model, or modify a schema, you need to know exactly what depends on it. Without that visibility, every change feels risky.

At Matia, we've heard this from data teams consistently: "We need to see how our data actually flows." Not after the fact, not inferred from stale metadata, but captured in real time as data moves through data pipelines, without logging into multiple tools.

So naturally, we did what we do best, and built it. 

Today, on the heels of our Series A Announcement, we're launching data lineage in Matia, end-to-end visibility into how data flows from source systems through dbt transformations to every downstream destination. With column-level precision and native dbt integration, you can trace any metric back to its source, understand what breaks when you make changes, and debug production issues in minutes.

Why Most Lineage Tools Fall Short

Most lineage tools work the same way: they reconstruct relationships after the fact by parsing SQL, scraping metadata from your data warehouse, or stitching together information from disconnected systems. The problem with this approach is that it's always playing catch-up. By the time lineage is generated, it's already out of date. And because these tools don't own any part of your data pipeline, they can only infer relationships; they don't actually know how data moved.

Matia is different. Because we own the ingestion layer and integrate natively with dbt, we capture lineage at execution time, as data actually flows through your pipelines. We don't infer lineage, we record it. This gives you column-level accuracy with zero manual configuration.

How Lineage Works in Matia

Matia's lineage shows you exactly how data moves through your stack:

Trace Backward to the Source

Start at any dashboard, metric, or AI model and trace backward through every transformation to the original source table. See which dbt models processed the data, what transformations were applied, and where the data originated.

Understand Forward Dependencies

Before you make a change, see exactly what depends on it. Deprecating a table? Matia shows you every dbt model, dashboard, reverse ETL sync, and ML pipeline that consumes that data. You'll know the blast radius before you ship.

Column-Level Precision

It's not enough to know that "Dashboard X uses Table Y." You need to know which specific columns flow through which transformations. Matia traces individual columns through your entire data pipeline, from customer_id in your Postgres database through dbt aggregations into the revenue metric on your executive dashboard.

Native dbt Integration

Matia automatically captures lineage from your dbt project. No manual tagging, no configuration; just connect your dbt instance and lineage updates in real time as your models change. See transformation logic, execution history, and dependencies directly in the lineage view.

Unified Operational Context

When you're investigating an issue, you don't want to jump between tools. Matia consolidates lineage with observability metadata including execution logs, data quality test results, freshness indicators, and row counts. Click into any node to see the full context without leaving the lineage view.

Real-World Use Cases

Before You Ship: Impact Analysis

You're refactoring 20 dbt models to improve performance. Before you deploy, Matia shows you exactly which dashboards, reverse ETL syncs, and downstream models depend on those tables. You identify two customer-facing dashboards that would break and adjust your plan accordingly. Ship with confidence instead of crossing your fingers.

During Incidents: Root Cause Analysis

A revenue dashboard is showing anomalous numbers. You trace the lineage backward and see that a dbt model failed its data quality checks three hours ago. The model depends on an upstream table that had an unexpected schema change. You identify the root cause in minutes, fix it, and communicate the issue to stakeholders with full context. 

For Compliance: Data Provenance

Your compliance team needs to document exactly how customer PII flows through your systems for a SOC 2 audit. Matia's lineage provides an auditable trail showing every transformation, who has access, and where sensitive data ends up. Generate compliance reports with one click instead of weeks of manual documentation.

For AI/ML: Model Debugging

Your ML model's predictions shifted unexpectedly. You trace lineage from the model features back to source tables and discover that an upstream data pipeline changed its logic. The feature engineering is correct, but the input data changed. You pinpoint the issue in minutes instead of days of investigation.

Built Into the Platform

Lineage isn't a separate tool you need to integrate. It's built into Matia's unified data platform. The same system that ingests your data, monitors pipeline health, and tracks data quality also captures lineage automatically. No additional setup.

This integrated approach means lineage works alongside observability. When you're investigating a data quality issue, you see lineage and execution metrics in the same view. When you're assessing the impact of a change, you see dependencies and freshness indicators together. Everything you need is in one place.

What's Next

Data lineage in Matia is available now for all customers. If you're already using Matia for ingestion or observability, lineage is automatically enabled, just navigate to any table or dbt model to see its dependencies.

We're continuing to expand lineage capabilities throughout 2026. Upcoming releases will include:

• Enhanced AI-powered lineage queries (ask questions in natural language)
• Cross-platform lineage for multi-warehouse architectures
• Lineage-based data cataloging and discovery
• Advanced impact analysis for breaking changes

If you're not yet a Matia customer and want to see how lineage can help your team move faster with more confidence, book a demo or start a trial.

For existing customers: lineage is live in your account now. Check out our documentation to learn how to make the most of it, or reach out to your customer success manager with questions.