What is data lineage, and why do you need it?

Organizations rely heavily on complex data pipelines to inform critical decisions. However, they also face mounting challenges from data sprawl.

As data flows through a complex web of systems, transformations, and dependencies, it becomes increasingly difficult to understand the data's journey from source to destination—and, ultimately, its overall trustworthiness.

This is where data lineage comes into play.

What is data lineage?

Data lineage is the process of tracking and documenting the journey of data from its source to its final destination. It provides a clear, end-to-end view of how data moves, transforms, and evolves throughout your organization.

Data lineage captures information, metadata about data sources, transformations, and dependencies between data objects. This enables teams to trace errors, assess impact, ensure compliance, and maintain trust in their data assets.

Why is data lineage important?

As data pipelines grow ever more complex, organizations face significant challenges in managing and governing their data effectively. Data lineage is a core tool in the data governance toolkit. However, not every organization prioritizes it as a critical component of their data strategy.

Data lineage is important because, without it, you are attempting complex data management with limited insight into your own data lifecycle. This makes life more difficult for everyone in your company that touches data:

• For data engineers and analytics engineers, the absence of data lineage makes their jobs significantly harder. When issues arise, they often have to spend hours or even days tracing data flows, identifying dependencies, and pinpointing the root cause of problems. This manual process is not only time-consuming and frustrating—it also takes them away from more strategic tasks aimed at delivering value to your business.
• Data analysts and business users also feel the pain of poor data lineage. They rely on data to make informed decisions. But if your data's trustworthiness is in question, they’re left second-guessing their insights. This uncertainty can lead to a lack of confidence in data-driven initiatives and a reluctance to fully embrace analytics.

Data lineage fundamentals

At its core, it’s about capturing and documenting the life cycle of data as it moves through an organization's systems and processes. This means data lineage can help solve the challenges of complex data pipelines.

Data lineage is like being a data detective. It involves following the clues (metadata) left behind as data flows from source to destination, through various transformations and dependencies.

There are three key components to data lineage:

Data origin

This could be a database, a flat file, an API, or any other source where data is initially captured or ingested.

Data transformations

As data moves through the pipeline, it often undergoes changes and manipulations such as filtering, aggregating, joining, or applying business logic. Each transformation leaves a trail of metadata that gets mapped by data lineage.

Data dependencies

Data often flows through multiple systems and relies on other datasets or calculations, creating a complex web of relationships. Data lineage tracks these dependencies by showing how data from one part of the pipeline impacts another.

By capturing these three components, data lineage creates a comprehensive map of your data's journey—kind of like a GPS for your data, allowing you to navigate the complex landscape of your data pipeline with ease.

Key benefits of data lineage

So why is this map so valuable? Let's consider a few key benefits:

Transparency

With data lineage, you have a clear, end-to-end view of your data flows. This transparency makes it easier to understand how data is being used, where it comes from, and how it's transformed. It's like shining a light into the black box of your data pipeline.

Traceability

When issues arise, data lineage allows you to trace the problem back to its source. It's like having a trail of breadcrumbs that leads you to the root cause of the issue. This traceability saves time and effort in debugging and ensures that problems are resolved quickly and effectively.

Impact analysis

Data lineage assists you in tracking and assessing the impact of changes to your data pipeline. For example, if you need to update a data source or modify a transformation, data lineage shows you exactly which downstream processes and reports will be affected. This impact analysis helps you plan changes more effectively and, maybe more importantly, avoid unintended data disasters.

Compliance

Data lineage is essential for demonstrating compliance with data governance policies mandated by government data handling and privacy regulations like GDPR and HIPAA. It provides an audit trail of how data has been used and transformed, making it easier to meet regulatory requirements and respond to audit requests.

Implementing data lineage in data governance

You can only reap these benefits once you have a solid data lineage practice built into your organization’s data governance. Here are a few key considerations for implementing data lineage:

Granularity

Determine the level of detail you need to capture in your data lineage. Do you need to track every single transformation and dependency, or can you focus on high-level flows? Are you using table-based lineage (which will show additions/deletions of fields at the table level) or column-based lineage (which will show changes to individual fields, such as a data type change)?

Automation

Manual data lineage tracking is time-consuming and error-prone. Look for tools that automatically capture and document data lineage as part of your data pipeline.

Integration

Data lineage should be integrated with your existing data management tools and processes. It should be easy to access and use for all stakeholders, from data engineers to business users.

Scalability

As your data pipelines grow and evolve, your data lineage solution needs to scale with them. Look for tools that can handle large, complex data flows and adapt to changing requirements.

dbt Cloud for data lineage

Gathering metadata and visualizing the relationships between data objects requires a solid set of tools. That’s where dbt Cloud can help.

At its core, dbt is a tool that helps data teams transform and manage their data in a more organized, efficient, and collaborative way. One of dbt Cloud’s key features is comprehensive support for data lineage that enables you to visualize and understand the relationships between your data models.

dbt’s Cloud built-in lineage features provide you with a bird's-eye view of the documentation and lineage of your entire data estate and a way to visualize and understand the relationships between your data models.

Defining model dependencies with ref() and source()

In dbt, you define your data models using SQL SELECT statements. But instead of referencing tables directly, dbt gives you two special built-in functions: ref() and source().

• The ref() function is used to reference other models within your dbt project, telling you this model depends on the output of that other model. When you use the ref function, dbt automatically infers the dependencies between models.
• The source() function is used to reference raw data sources, such as tables in your data warehouse. It's like acknowledging the starting point of your data's journey.

By consistently using ref() and source() throughout your dbt project, you're creating a clear map of how your data flows from source to end result.

Visualizing the DAG (Directed Acyclic Graph)

With your model dependencies defined, dbt can now generate a visual representation of your data lineage. This is known as the DAG (Directed Acyclic Graph) — a diagram of data relationships and connections, displayed as an interactive web page. Each node in the graph represents a model, and the arrows between nodes represent the dependencies between them.

This visual representation is incredibly powerful. It allows you to see, at a glance, how your data is transformed and how changes to one model might impact others downstream like the ripple effects of a pebble dropped into a pond.

But the DAG is more than just a pretty picture. It's also a valuable tool for debugging and troubleshooting. When you encounter an issue with one of your models, the DAG guides you in tracing the problem back to its source.

Enhancing data lineage with documentation and tests

While the DAG provides a high-level view of your data lineage, dbt offers additional features to enrich and validate your understanding of the data.

First, there's documentation for your dbt models. Good documentation for your dbt models will help downstream consumers discover and understand the datasets you curate for them.

dbt provides a way to automatically generate documentation for your dbt project and render it as a website—creating a shared understanding of your data that anyone on your team can reference. It's like publishing a textbook for your data estate, explaining the purpose and functionality of each piece of the pipeline

But documentation only goes so far. To truly trust your data, you need to test it. Fortunately, dbt makes it easy to test smarter, not harder, by defining and running tests on your models. dbt provides a simple way to define and run data tests to validate your transformations and catch potential issues early on as a built-in part of your workflow.

Conclusion

Data lineage is an essential component of modern data management. The ripple effects of poor lineage tracking lead to:

• Analysts spending hours debugging transformation issues
• Teams struggling to understand dependencies
• Your organization making decisions based on what might be incomplete or incorrect data

Without proper lineage tracking, a minor data discrepancy can cascade into a problem with significant business impact.

By providing end-to-end visibility into the data pipeline, data lineage helps your organization overcome the challenges of complexity, ensure data trust, and make informed decisions. Just as a GPS helps navigate unfamiliar roads, data lineage acts as a guide through the complex landscape of data transformations and dependencies.

With dbt’s intuitive approach to data transformation and built-in lineage features, you can create a data pipeline that is transparent, traceable, and trustworthy. But the real power of dbt's lineage features is how, by making your data lineage clear and accessible, you're empowering your entire team to work with data more effectively.

Learn more about how dbt can help you deliver data products that people trust—ask us for a demo today.