Data lineage: What is it and how to implement it

Have you ever been to a family gathering and thought, “Who are half of these people? How are we all even related? I need a family tree ASAP.”

Swap people for data, and that’s a lot like what data lineage does. At any point in your data’s lifecycle, data lineage helps you see where it comes from, where it goes next, and how it relates to any other data in your system.

For data teams that are working around the clock to pinpoint issues and figure out what else is affected by them, data lineage is a necessity.

Let’s dive deeper into what data lineage is, why your data team will be better off with it, and how to implement it for your organization.

What is data lineage?

Data lineage is the process of tracking the movement, transformation, and relationships of data as it flows through different systems. By visually laying out the flow of your data, you can track it from its origin to its final destination—seeing where it stops and how it’s transformed along the way.

By gaining this level of visibility across every stage of your data’s lifecycle, you and your team are able to:

• Manage incidents by analyzing the root cause and impact of each incident
• Guide migrations and better understand the impact of changes before they’re published
• Maintain compliance for better data governance
• Help new data team members and stakeholders gain awareness of data flows
• Optimize your data flow by reducing inefficiencies or redundancies throughout your data infrastructure

To better understand how data lineage works in practice, let's consider an example from a retail company.

A data lineage example 

Let's say you’re on the data team at an eCommerce business, and you have an `orders` table to track all of your orders. Upstream lineage goes all the way back to the source, showing you which tables were transformed to calculate that `orders` table. Downstream lineage shows you which tables are dependent on that table now, giving you an idea of what would be affected by a change to that table.

Together, along with the rest of the datasets within your flows, you have an end-to-end data lineage graph.

Table-level vs. column-level data lineage

Before we go further, it’s worth it to note that there are two main types of data lineage: table-level and column-level.

Table-level data lineage is a bit like an overview map of a city. It focuses on how entire tables relate to each other without getting into the specifics of which individual columns within a table are involved.

Column-level is more of a street-level view. It focuses on the movement and transformation of each column of data in a table, giving you a more granular look.

Both are important and serve different functions, depending on what level of detail you need.

Benefits of implementing data lineage

Data lineage is more than just a nice-to-have best practice. If you want your data to be used and relied upon to make informed decisions, you need to have a lineage system in place. Not only does it help ensure data quality, but it also helps boost data trust within your organization.

1. Improved data quality

No matter how well-designed and maintained your data stack is, errors will happen. Whether it’s an integration issue or a fat-fingered 0 when manually entering data, there’s always something to catch. To solve these problems quickly, teams need a clear data lineage.

With clear upstream and downstream views of your data at any point in its lifecycle, you can save hours of manual backtracking and maintain data quality throughout the pipeline.

2. Root cause analysis

Data lineage can help data engineers improve their data quality by making it easier to conduct root cause and impact analyses.

In the same way that you can use a family tree to figure out who someone is and how they’re related, data lineage helps you understand how your data is related. So when you’re resolving an issue—whether it’s an issue with the data source or its impact downstream—you’ll have a visual map to help you find the source of the problem and see what else might be impacted. 

This will be helpful not only in fixing the problem but also in proactively alerting stakeholders so they don’t lose trust when they see that a dashboard is down.

3. Regulatory compliance

Companies that comply with regulations like GDPR or HIPAA need to be able to answer the question Where does your data flow? Data lineage makes answering that question easy.

Data lineage makes your flow of data traceable, helping with audits and regulatory checks. With a clear record of your data, you can show where it came from, how it has been transformed, and who accessed it. This can help your team confidently and efficiently ensure compliance and avoid penalties by meeting regulatory obligations. 

4. Enhanced data governance

Data governance is all about managing data availability, usability, and security. With data lineage, organizations can effectively enforce governance policies. By understanding the flow of an organization’s data and implementing proper access controls, data engineers can ensure the security of sensitive information and mitigate the risk of unauthorized access.

5. Optimized data management

While a lot of the benefits of data lineage are reactionary, there are ways you can use it to proactively improve your data process as well. When you have your data laid out visually, it’s a lot easier to spot bottlenecks and inefficiencies that you might not notice otherwise. 

This high level of data visibility helps teams get better with planning, reporting, and forecasting. In the end, better data management results in operational efficiency and improved decision-making. 

Why you should automate data lineage

Your data ecosystem has huge amounts of data moving through it at a rapid pace, and trying to manually create data lineage to track it will eventually end up a losing battle. As your data flows scale, it’ll only become harder to map everything. Your map gets bigger, and changes become more frequent, only adding to the work that goes into maintaining data lineage.

Automation helps with this—allowing organizations to maintain accurate, up-to-date data lineage as their data ecosystem scales. Automated tools can help ensure data accuracy by consistently tracking data movements without human intervention.

Automating data lineage offers several key benefits:

• Minimizes human errors by automatically tracking data movements and transformations.
• Makes troubleshooting faster by quickly pinpointing where problems in the data flow started.
• Enhances compliance with audit trails that are automatically generated and updated.

Steps and best practices to implement data lineage

The specific steps toward implementing data lineage are going to look different based on your data stack and what you need to get out of your data. That said, there are a handful of steps you can take to make sure things go smoothly as you implement data lineage in your organization.

1. Identify the business use case for your data lineage

Like your data, lineage is only helpful if it solves the problem it’s needed for. No matter how good your data practices are, if you’re not pulling the right data or formatting it in the right way, it won’t be helpful. Too much unnecessary detail can complicate things and overwhelm the end result.

Defining what your data lineage is going to be used for will help you know what level of granularity you’ll need, so make sure you communicate with your data customers (stakeholders) to figure out what they need before you get started.

2. Before building something custom, get what you can for free

It can be appealing to build a query parser from scratch for the sake of flexing your technical skills, but if it’s possible, see what you can get away with using free resources. For example, if you can use an open-source library like sqlglot to get column-level lineage or dbt to get table-level lineage, do that first.

If you try that and find out you need a deeper level of granularity, that’s when you should consider building something bespoke.

3. Automate as much as you can

As your data system scales, it’s going to become harder and more time-consuming to keep an accurate pulse on your data. Not only is tracking it manually going to eat up more and more of your time, but it’s also susceptible to human error.

Using an automated data lineage tool helps you map your data lineage automatically—both by parsing your data and laying it out visually. This is beneficial for a few reasons:

It’ll give you time back to focus on more important, high-impact projects

1. It’ll be more accurate
2. When someone leaves their role, they won’t take the knowledge of your data’s lineage with them

4. Make sure the format matches the use case

We’ve talked a lot about business use cases, but it’s because it’s really important. A lot of data teams go wrong by building something robust that far exceeds what’s actually needed.

While it might be tempting to use a graph database like Neo4j, that would require that your team learn how to query Neo4j. Whereas, if you’re already using a data warehouse like Snowflake and you can put the lineage there, simply do that. If you need something more specialized, you can do that later, but you should only make things as complicated as they absolutely need to be and nothing more.

Wondering how to implement data lineage in Snowflake? Check out our guide.

5. Get one use case working end-to-end, then move on to the next

When you’re getting started with data lineage, it’s tempting to try and apply it universally across all your different flows right away. This isn’t the most helpful approach. Sure, you could solve a bunch of different use cases at a one-inch depth, but what if they need a mile?

It goes back to making sure you’re fulfilling the business use case. Sure, you could cover everything quickly and check off the box that you’re “doing” data lineage—but is it helping anyone?

Instead, try picking one use case, implementing data lineage end-to-end, and when you know that’s working, move on to the next. Better to move thoughtfully and make sure it’s usable than cover all your bases with a half-implemented lineage that no one will get value from.

Focus on one use case first, then address other use cases. 

Automate data lineage seamlessly with Metaplane

Your data is a massive web of movement, transformations, and dependencies. Data lineage helps you make sense of it all—making your data visible across its entire lifecycle so you can better understand, troubleshoot, and resolve issues.

With Metaplane, you can streamline the data lineage process with table-level and column-level lineage across your entire data stack—no manual setup required. Gain complete visibility upstream for faster root cause analysis and downstream for instant impact assessments. 

‍Try Metaplane today for full visibility and control over your data lineage.