Data Catalog vs. Data Lineage: Differences, Use Cases, and Evolution of Available Solutions

Published on: February 28th, 2023, Last updated on: February 22nd, 2023

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The main difference between a data catalog and a data lineage is that a data catalog is an active and highly automated inventory of an organization’s data. It enables search, and discovery, and drives end-to-end data operations. On the other hand, data lineage is a map of how all this data flows throughout your organization.

With a data catalog, you can determine what your data is and how you can best put it to use. With data lineage, you know where your data is - who’s producing it and who’s consuming it.

Forrester wave report on enterprise data catalogs

Data catalogs and data lineage are indispensable for any business that deals in large volumes of data. They can alert business users to issues - such as outdated or problematic data in reports - that could otherwise lead to mistaken and costly decisions.

To understand data catalog vs. data lineage better, let’s examine in detail how the two work synergistically together to empower employees to discover, access, and utilize the right data.


Table of Contents

  1. What is a data catalog?
  2. What is data lineage?
  3. Data catalog and data lineage: Enabling efficient use of data
  4. Future of data catalogs and data lineage
  5. Conclusion: data catalog vs. data lineage
  6. Data catalog vs. Data lineage: Related reads

What is a data catalog?

A data catalog is a workspace where an organization’s users can discover and annotate data. Data catalogs contain rich metadata about a given asset’s origins, purpose, and business context. They also provide fine-grained permission controls and a collaborative environment that supports editing across the company.

Data catalog integrating with diverse data sources and data tools. Source: Atlan

Data catalog integrating with diverse data sources and data tools. Source: Atlan

The modern “third-generation” data catalog enables everyone from product managers to data scientists to track and collaboratively maintain an organization’s diverse data assets at scale. Data lineage tools then use techniques such as SQL parsing, API crawling, and custom API ingestion to track and visualize the history of these assets as they flow through the company.

Companies can select from a wide variety of open-source and commercial data catalog solutions to implement a solution that meets their needs.

What is data lineage?

Data lineage tracks the lifecycle of your data. A good data lineage implementation tells you the origin of your data and how it’s changed over time. It tracks both table-level lineages as well as column-level lineage, so you can see how your organization’s data structures change over time.

Automated data lineage: Understand how data flows from the source to the dashboards. Source: Atlan

Automated data lineage: Understand how data flows from the source to the dashboards. Source: Atlan

Data catalogs and data lineage together solve the problem of metadata management. A data catalog centralizes critical business information in a single source of truth. Lineage provides confidence that data is up to date and enables tracing the impact of any changes across the company.



Data cataloging and data lineage: Enabling efficient use of data

Every company produces tons of data. And every company needs a way to account for it. The difference between a data catalog vs. data lineage lies in how both contribute to solving this problem at scale.

The data catalog provides the foundation for describing and securing access to data. Enterprise data catalogs are used by a diverse range of business users - data scientists, engineers, product managers, and executives - to discover data and generate insights essential to running the business.

Once that’s in place, a company can begin tracking where that data came from and - just as importantly - who’s using it, both upstream and downstream. A good data lineage tool will show a record of your data’s journey across the company over time. It enables users to identify the root causes of problems, assess the impact of changes, and implement end-to-end data governance.


Speeding up Root Cause Analysis with Lineage -Atlan Demo Series


Both of these pieces - data catalog and data lineage - are essential for meeting many business compliance requirements. Take the European Union’s General Data Protection Regulation (GDPR). Under GDPR, you need to know what data is Personally Identifiable Information (PII) that belongs to a customer. You also need to know all the locations that data lives - databases, support tickets, report caches, cold storage, etc. - so you can purge it upon request.

A data catalog answers the first question. Data lineage answers the second.

This case of Postman, makers of the popular API development and testing tool, shows how a data catalog and data lineage evolve together in practice.

The company originally struggled with duplicated metrics and daily Slack inquiries from users about data providence. Data duplication and user confusion sowed distrust in the company’s data.

As Postman’s Prudhvi Vasa puts it, “building trust is hard, but losing it is easy—it just takes one mistake.”

To solve this, it first tried cataloging data in a Confluence document, and then Google Sheets. It quickly outgrew both solutions. They eventually moved to Atlan as their dedicated data cataloging solution.

Once the company had its data in a data catalog, it built its lineage system over it. This involved gathering information on their data’s origins (a process you can perform manually but that’s accomplished much more efficiently via automation.

Postman could finally ask - and answer - questions about their data’s origins and its interconnections. Users could now not only discover data but understand how a proposed change to a data asset would affect other assets and users across the company.

Netflix faced and solved a similar set of challenges. In their case, they worked backward from the goal of developing a comprehensive data lineage network. That first entailed creating a flexible data catalog that could represent the company’s diverse assets. They then developed a separate data lineage model that users can navigate via a graph database.

Future of data catalogs and data lineage

Creating a data catalog is only the first step. No one will use a data catalog unless it’s usable. And that means moving away from the notion of a data catalog as a standalone entity.

Data catalogs will continue to evolve to bring metadata to users where they work - e.g., in Slack or their favorite BI tool. Data catalogs will become repositories of active metadata that cut across all of a company’s data infrastructure and business productivity tools.

Similarly, data lineage networks are indispensable - but only if users can easily visualize the journey their data takes. While a simple flow visualization is a good start, we’ll see more data lineage systems take Netflix’s lead, representing lineage flows as searchable, navigable graphs.

Conclusion: data catalog vs. data lineage

In today’s businesses, data is repeatedly imported, exported, transformed, stored, and displayed in multiple, redundant ways. Without a way to catalog and trace this movement, finding accurate data can become a nightmare - and compliance becomes nearly impossible.

Understanding data catalog vs. data lineage means understanding the two different but essential roles each plays in solving this problem. A data catalog tells you what you have. Data lineage tells you where everything is and how it all relates. Using both solutions, your company can ensure data accuracy, implement data governance, and manage business change.

Want to go deeper? Learn more about the modern data stack and its components - and how data catalog and data lineage fit within the larger picture.


Ebook cover - metadata catalog primer

Everything you need to know about modern data catalogs

Adopting a modern data catalog is the first step towards data discovery. In this guide, we explore the evolution of the data management ecosystem, the challenges created by traditional data catalog solutions, and what an ideal, modern-day data catalog should look like. Download now!

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