Data Insights

Data Lineage: The Unseen Lifeline of Data-Driven Organizations

Thalia Barrera
May 30, 2023
12 min read

Data lineage might not be the flashiest term you'll encounter in the realm of data engineering, but it's undeniably one of the most consequential. Behind the screens of the data-driven world, data lineage plays a pivotal role in illuminating the journey of data - its origins, transformations, and destinations. Understanding it isn't just beneficial, it's crucial to harnessing the power of data effectively.

In this blog post, we'll delve into the depths of data lineage, exploring its significance, the challenges it presents, and the opportunities it offers in the modern data landscape.

Whether you're a seasoned data engineer or a curious enthusiast, this exploration will shed light on why data lineage has become a cornerstone of successful data management.

What is Data Lineage?

In essence, data lineage is the life story of your data. It describes where your data comes from, where it moves, and how it changes as it travels through various processes and systems within your organization. It provides a traceable route, marking the data's origin, the transformations it undergoes, and its final destination.

Think of data lineage as a detailed map of your data's journey. Just like a geographical map outlines routes, landmarks, and destinations, data lineage charts the path that data takes.

This map is an invaluable tool for anyone who works with data, from data engineers and data scientists to business analysts and decision-makers. It allows them to navigate the vast landscape of data with confidence, understanding not just the data's current state, but its history and trajectory as well.

By making the journey of data traceable and transparent, data lineage plays a critical role in establishing trust in data. It answers crucial questions like: Can we trust the data presented in this report? Where did it originate? What changes has it undergone? How recent is it? In doing so, data lineage lays the foundation for data integrity, accuracy, and reliability - the cornerstones of any successful data-driven organization.

The Imperative of Data Lineage

Data lineage plays a critical role in various aspects of data management and governance, underpinning the integrity and trustworthiness of your data infrastructure.

Improving Data Quality

By tracing the journey of data, you can identify points where errors or discrepancies occur, which are often invisible in the end-results. This allows you to correct issues at their source, improving overall data quality.

Risk Management and Impact Analysis

Before making changes to a data system, data engineers need to understand the potential impacts. Data lineage can highlight which datasets, reports, or applications will be affected by a proposed change, allowing for effective risk assessment and change management. Furthermore, in the event of a data breach, data lineage can help identify which data was compromised and assess the breach's extent.

Regulatory Compliance

The stringent regulatory landscape, brought upon by laws such as GDPR and CCPA, makes data lineage a crucial tool. These regulations require organizations to have a clear understanding and documentation of what data they have, where it's coming from, where it's going, and how it's being processed. Data lineage provides a clear map that can demonstrate compliance during audits and investigations.

Optimizing Data Flows

Another noteworthy perspective comes from Borja Vazquez, a prominent data engineer, who emphasizes the crucial role data lineage plays in understanding and improving data pipeline performance.

Borja points out: "Execution times, tasks duration, costs and resources consumption are key to understanding how data pipelines perform. By using this data it’s possible to extract metrics pointing at the slowest models, or the ones that are consuming the most resources. However, these metrics are usually reported in isolation. If a model takes longer to run they don’t give a view on what’s the actual impact on the whole pipeline."

Borja also suggests a more integrated approach to this challenge: "Overlaying performance data over a DAG can provide a much richer understanding of how single tasks can affect the whole data pipeline performance."

Onboarding new hires

Another less-discussed yet valuable aspect of data lineage is its role in knowledge transfer, especially when it comes to onboarding new team members.

As Gabs Ferreira states: "It takes time for new hires to properly understand a codebase and it's not uncommon to commit mistakes from lack of knowledge. With self-documented pipelines provided by lineage, the knowledge transfer of the domain doesn’t depend only on people."

In other words, by visually representing the data journey from its origin to its destination, data lineage facilitates new hires' understanding of complex data pipelines. This reduces the likelihood of errors born out of unfamiliarity with the system and ensures that knowledge isn't lost when employees move on.

The Anatomy of Data Lineage

Just as every journey varies in complexity and direction, so too does the journey of data within an organization. These diverse paths give rise to different types of data lineage, each offering unique insights into your data landscape. Let's dissect the three main types: end-to-end lineage, vertical lineage, and horizontal lineage.

End-to-End Lineage

End-to-end data lineage provides a broad overview of the data journey. It’s like viewing a long cross-country trip from a bird's-eye perspective - you see where the journey started, the path taken, and where it ended up.

In the context of a data pipeline, end-to-end lineage would show the initial data sources, the transformations applied, and the final reports or dashboards where data is consumed. This type of lineage is often used in data governance to ensure regulatory compliance, as it provides a clear and concise view of the data lifecycle.

Vertical Lineage

Vertical data lineage dives deeper into data's journey within a specific system or process. Like taking a closer look at a specific section of the journey, it reveals the exact roads traveled and sights seen. In terms of data, this means understanding the transformations that happen to data within a particular database or application.

For instance, within a data warehouse, vertical lineage would show how data from an initial table is transformed, aggregated, or joined to create a new table or view. This detailed view is particularly useful when troubleshooting data quality issues or optimizing specific processes within a system.

Horizontal Lineage

Horizontal data lineage, on the other hand, focuses on data movement across systems. It’s akin to mapping the journey from one city to another, showing the transitions between different modes of transport. In data terms, horizontal lineage illustrates how data moves from one system to another - for example, from an operational database to a data warehouse, or from a data warehouse to a BI tool.

This form of lineage helps data engineers understand dependencies between systems, making it essential when planning system migrations or integrations. Additionally, it supports impact analysis, allowing engineers to see how changes in one system might affect others.

Column-level data lineage

While understanding data lineage at a high level gives us insights into how data is transformed from source to target systems, it's often necessary to dive deeper. That's where column-level data lineage comes into play. This form of data lineage tracks the life cycle and transformation of specific columns in a dataset.

For instance, suppose you're working with a customer dataset. The customer's total expenditure value might be derived from multiple sources: online purchases, in-store purchases, and perhaps even external datasets. To verify the accuracy of this data, you would need to track the lineage of the 'total expenditure' column across all these sources.

Challenges in Implementing Data Lineage

While the benefits of data lineage are clear, implementing it in practice can present several challenges. Understanding these hurdles is the first step towards addressing them effectively.

Complexity of Data Systems

Modern data systems are increasingly complex and interconnected. Data can originate from a multitude of sources, undergo various transformations, and be consumed in different forms by diverse applications. This complexity makes tracking data lineage a considerable challenge, requiring sophisticated tools and methodologies.

Lack of Standardized Tools

There isn't a one-size-fits-all tool for data lineage. The best tool for the job often depends on the specifics of your data architecture and the nature of your data. Different tools have strengths in certain areas, such as end-to-end lineage, column-level lineage, or visualizing data lineage, but may fall short in others. Additionally, data lineage tools need to integrate with your existing systems.

Evolving Data Regulations

Data regulations like GDPR, CCPA, and others are constantly evolving, making it challenging for organizations to stay compliant. These regulations often need detailed data lineage to demonstrate compliance, putting pressure on organizations to keep their data lineage practices up-to-date.

Data Volume and Velocity

The sheer volume of data that organizations deal with today, combined with the speed at which new data is generated, can make comprehensive data lineage a daunting task. Keeping track of data lineage in real-time or near-real-time scenarios can be particularly challenging.

Resource Intensive

Implementing and maintaining data lineage is not a trivial task. It requires significant resources, including specialized skills. Smaller organizations or teams with limited resources might struggle to allocate sufficient resources towards data lineage.

Leveraging dbt for Data Lineage

The open-source tool dbt (Data Build Tool) is widely used by data engineers to transform data in their warehouses. Beyond its robust transformation capabilities, dbt also offers functionalities for documenting and visualizing data lineage.

dbt models are essentially SQL select statements that create tables or views in your warehouse. 

Since these models are code, dbt can parse your project and understand the dependencies among your models. dbt uses this information to generate a directed acyclic graph (DAG) that visualizes your data transformations' flow.

For example, let's consider a simple data transformation flow in a retail company. You might have raw data models for sales, customers, and products. Using dbt, you could create transformation models that aggregate this raw data into more useful views. A monthly_sales model might join data from sales and products, while a customer_lifetime_value model might use data from sales and customers.

Using the ref() function in your dbt models, you specify dependencies between models. For instance, in the monthly_sales model, you'd use ref('sales') and ref('products'). dbt uses these ref calls to construct your project's DAG. Not only does this graph help you understand your data transformations better, but it also allows dbt to intelligently run models in the correct order based on their dependencies.

While dbt does not natively support column-level lineage, several tools integrate with dbt to provide this capability, such as Tree Schema and DataHub. These tools extract column-level lineage information from your dbt models, letting you dive deeper into your data's life cycle.

Tools for Data Lineage

Implementing data lineage can be simplified with the right tools. These can range from open-source solutions to proprietary offerings. Here are some notable options:

  • Collibra - An enterprise-grade data intelligence platform that includes data lineage capabilities.
  • Azure Purview - A product from Microsoft's Azure suite, is a unified data governance service that aids in managing and governing data across an organization.
  • Open Metadata - An open standard for metadata and data lineage, designed to provide a comprehensive view of the data in your organization.
  • MonteCarlo - A data reliability platform that aims to increase trust in data by minimizing downtime and improving its reliability. One of the key offerings is its data lineage capabilities.
  • Alvin: An AI-powered data lineage tool that automates mapping and provides actionable insights to ensure data quality and governance.
  • Marquez - An open-source project focusing on metadata service and data lineage.
  • Apache Atlas - An open-source data governance and metadata framework that provides comprehensive data lineage features.

Real-World Use Cases of Data Lineage


Data lineage plays an instrumental role across various industries and domains. One prominent example comes from the entertainment streaming giant, Netflix.

Netflix's Data Engineering and Infrastructure (DEI) team recognized the value of data lineage in understanding data dependencies and its impact on various aspects like Detection & Data Cleansing, Retention & Data Efficiency, Data Integrity, Cost Attribution, and Platform Reliability. The team envisioned and embarked on the development of a comprehensive data lineage system a few years ago.

In their words, their mission statement to guide their investments in data lineage was:

“Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.”

This statement underscores the importance of data lineage not only in technical aspects like data cleansing or platform reliability but also in strategic decision-making. With a well-implemented data lineage system, decision-makers are better equipped to make informed decisions that can ultimately 'win moments of truth' - critical junctures that can determine the success or failure of a particular initiative or strategy.

The utility of data lineage extends across multiple industries and applications. Let's explore some broad use cases by industry:

Financial Services

In the financial services industry, regulatory compliance is paramount. Regulations like Basel III and the Dodd-Frank Act require financial institutions to maintain a clear understanding of their data's journey. Data lineage can provide the necessary transparency, tracing data from initial transaction records through to risk reports and regulatory filings.


In healthcare, data lineage plays a crucial role in patient data management and research. With stringent laws like HIPAA governing patient data privacy, understanding how and where patient data moves is essential. For example, data lineage can trace a patient's health data from admission records through to diagnosis, treatment, and billing systems. This not only ensures regulatory compliance but can also contribute to improved patient outcomes by highlighting data inconsistencies or errors.


For retail businesses, understanding customer behavior is key to driving sales and improving customer satisfaction. This often involves complex data analytics, integrating data from various sources like online transactions, customer feedback, social media, and in-store sales. Data lineage helps maintain the accuracy and trustworthiness of this integrated data by tracing each data element back to its source, thereby ensuring reliable insights for decision-making.

The Future of Data Lineage

As the world continues to become more data-driven, the role of data lineage is poised to become even more critical. Here's a look at some of the trends and developments we might see in the future of data lineage:

Automation in Data Lineage

Automated data lineage tools will not only capture and document data lineage more efficiently but will also keep it up-to-date in real-time as data sources, systems, and transformations evolve.

Dan Mashiter, a leading voice in data engineering, illuminates this potential:

"As automation is becoming more prevalent in all aspects of software engineering, it’s natural that data engineering picks up the same practices; from DevOps to DataOps. High quality lineage data will enable similar use cases in data engineering. Companies already run tests for data pipelines, as well as QA testing, and we see some exciting possibilities here when it comes to impact analysis."

Integration of Machine Learning and AI

Barr Moses, a recognized leader in the data management field, reflects on how data lineage practices have evolved over time:

"In the old days (way back in the mid-2010s), lineage happened through a lot of manual work. This involved identifying data assets, tracking them to their ingestion sources, documenting those sources, mapping the path of data as it moved through various pipelines and stages of transformation, and pinpointing where the data was served up in dashboards and reports. This traditional method of documenting lineage was time-intensive and nearly impossible to maintain."

Indeed, the laborious, manual task of tracking data from origin to consumption posed significant challenges, making it difficult to maintain accurate, up-to-date lineage information.

However, Moses notes a paradigm shift with the advent of new technologies: "Today, automation and ML have made it possible for vendors to begin offering data lineage solutions at scale. And data lineage should absolutely be a part of the modern data stack." This trend will continue to accelerate dramatically.

Expansion of Column-Level Lineage

Column-level lineage, which traces the journey of individual data elements, is likely to become more prevalent. This detailed view can provide valuable insights for data quality, data governance, and impact analysis. As tools and methodologies for column-level lineage improve, we can expect it to become a standard feature of data lineage.

Greater Emphasis on Data Privacy and Security

As data privacy and security continue to be a major concern, data lineage will play a crucial role in addressing these issues. By providing a clear map of data flows, data lineage can help identify potential security risks and ensure that data privacy requirements are met. This will be particularly important in industries with strict data regulations, like healthcare and finance.

Increased Use of Visualizations

Visualizations are a powerful tool for understanding complex data flows, and their use in data lineage is likely to increase. Future data lineage tools may offer sophisticated visualization features, making it easier for both technical and non-technical stakeholders to understand data lineage.

Cross-Functional Collaboration

As the importance of data lineage is recognized across organizations, we can expect to see more cross-functional collaboration around it. This could involve data engineers working closely with business analysts, data scientists, and decision-makers to ensure that data lineage serves the needs of all stakeholders.

Conclusion: The Crucial Compass of Data Lineage

In the vast seas of our data-driven era, data lineage acts as a crucial compass. It navigates the complexity of our data systems, tracing data journeys from source to destination, and illuminates our path to meaningful insights.

This blog post has dissected the anatomy of data lineage, unveiled its challenges, and explored tools that aid its implementation. We've seen its real-world impact across industries and glimpsed its promising future.

However, beyond the tools and techniques, the core of data lineage lies in robust data governance. These practices form the bedrock for building effective data lineage.

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