There is a famous saying that goes by:

“The journey is more important than the destination."

Coincidently, this is also true for data in modern times. The information which we see in pretty reports and charts or is displayed to users via an application has actually experienced a long run of data processing and transformations. These transformations are a result of well-planned ETL pipelines and data management strategies. Originating from different touchpoints, data witnesses several alterations throughout its lifecycle, such as:

  • Joining with information from other sources.

  • Data type manipulation.

  • Removal of irrelevant and unrequired information.

  • Filtering rows.

Such alterations are required to maintain data quality and to define a standard schema throughout the organization. However, at times it becomes important to keep track of these changes for various purposes. The changes that information undergoes throughout its journey are called data lineage. This article will serve as the ultimate guide to data lineage for beginners as well as experienced professionals. But first, let’s talk more about what this lineage is.

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Table of Contents

What is Data Lineage?

Data lineage is quite a self-explanatory term. Lineage refers to tracking one’s pedigree or ancestry, so, in the context of data, it means tracking data flow throughout its life cycle. The procedure keeps track of where the data originated, all the databases it has been stored in, all the transformations applied to it, and its final destination.

Data lineage tools act as a version control system (VCS) for the data, tracking every formation data has seen, highlighting key changes, and providing easy access to previous versions for debugging. These also help engineers with visualization of the entire journey in the form of a map, linking each data stop to the other. The key metric which allows data lineage tools to conjure important information about the data is its metadata. Metadata contains the complete description of what the data table contains, including schema information, formats, creation date, modification date, file size, and authors. This helps users track the changes made to the stored data and at what time.

Data lineage seems like an unnecessary effort for seemingly no gain, but it has more advantages than you think. Let’s look over some of the problems it tackles.

What Problems Does Data Lineage Solve?

Data growth brings opportunities, but it also spins up certain challenges. With colossal data pipelines, it becomes all the more difficult to diagnose and pinpoint where a problem occurred. Data lineage solutions solve a lot of such problems.

Better Understanding of Data

Data lineage gives users a snapshot view of all the versions of the dataset that have been previously created. By analyzing the journey, it makes more sense to data engineers as to why the data transformations were made. This helps with data discovery and helps engineers understand its potential. Being aware of the data origin helps stakeholders familiarize themselves with the data sources and touchpoints. This can help with infrastructural benefits such as eliminating data silos.

Data-Driven Decision-Making

Having a better understanding of the data automatically leads to better decision-making. When you know what information your data holds, you know how to drive value out of it. With the help of data science, analysis, and data lineage, organizations can dive into new business initiatives and ventures.

Impact Analysis

Data lineage helps organizations deal with the ripple effects that may occur from changing any step in the ETL pipeline. With the complete data map at their disposal, data engineers can analyze how a certain action or change will affect the entire data life cycle and what repercussions it will entail for the entire database.

Better Error Handling

When your data pipeline contains 10’s of data modification points, debugging becomes extremely difficult. You can see that the final output is not as you desire, but you have no clue where things went wrong. Data lineage solves this by presenting you with individual datasets resulting from each modification in the pipeline. This makes it easier to view the alterations applied and focus on the erroneous SQL queries.

Training Employees

When a new employee joins the data team, the biggest challenge is transferring the domain knowledge. For most enterprises, data is scattered and undergoes multiple transformations for different uses. Tracking data lineage means you will have an easy path for your trainee to follow. This centralized data catalog containing information about all data assets, touchpoints, and alteration procedures aids the learning process.

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Where Does Data Lineage Fit into Data Governance?

Data governance is an important process in data warehouses and in big data infrastructures such as data lakes. It gives data a much-needed structure by defining user roles (owner, read-only access, etc.), processing rules, policies, and data security. A defined structure helps with easy data management and maintenance.

Data lineage can be seen as an aid to data governance. Where governance defines multiple rules and restrictions for data, lineage helps determine whether these are being complied with or not. Data lineage provides managers with an audit trail of all the transformations data has gone through, which is used to verify that all processes are in order, as defined by data governance.

Data Lineage Methods & Techniques

There are multiple ways in which data lineage can be tracked. Let’s talk about them below.

Data Tagging

Whichever transformation engine you use tags the data along its transformation journey. Each version of the data is tagged separately to create a link between them. These tags are then used to create a lineage for the data. The downside of this approach is that all workflows must be defined within that tool, and any transformation that occurs on the data externally will not be tracked.

Pattern-Based Lineage

This form of lineage tracking is dependent on the metadata of the tables and the databases. It does not concern itself with any programming language or the code utilized in data transformation but rather the changes made to the metadata. It tracks these changes throughout the data lifecycle to create a lineage for it.

It is a very simple approach that is environment independent since it only concerns itself with the data, so it can be used across multiple database systems such as Oracle, MySQL, and SQL Server. However, its simplistic approach does make it prone to errors as there always be a chance that it misses certain complex patterns that are very subtle.

Lineage By Parsing

This is the most effective method of lineage tracking. It deals directly with the logic written to perform the necessary transformation. The code logic is automatically read and used to keep track of all the alterations in the data. This form of lineage tracking is complex to implement because it requires knowledge of the programming environment and logic used for the transformations.

How Can Data Leaders Get Started With Data Lineage?

The general approach to implementing a new concept is by first establishing its need. For data use cases, this is mostly not a requirement because having better data management and infrastructure benefits an organization one way or the other. The same rule applies to Data lineage. In 2018, the European Union (EU), introduced the General Data Protection Regulation (GDPR). This is a data protection law that requires organizations to focus on Data lineage, so it has become more important for leaders to implement it. Certain steps are required before data lineage can be put into practice.

  1. Identify Important Data Tables: It is important to first gather information regarding all the data that is stored in its final form. This is important for tracking the lineage.

  2. Track To The Source: For each data table, track all its transformations and intermediary forms. Create tags for all the transformations, the authors of the scripts, and all the storage locations.

  3. Create a Map: Link all the tags to create a map that represents the data flow throughout the organization.

As vital as it may be, creating a data lineage is a frustrating and daunting task, especially if your data is widespread.

Data Lineage Best Practices

Data Lineage tracking is a tough task however certain practices make the overall process easier and more efficient.

  1. Track On-The-Go: Lineage should be maintained as the data moves along the ETL pipeline. Tags should be created as soon as data passes through a certain script. Waiting for the entire procedure to complete can make the tagging process challenging.

  2. Automation: Keeping track of all the changes is a tough task, so automation is always the best solution. There are multiple tools and cloud services available that perform automatic data lineage tracking.

  3. Put Data Lineage to Use: As data lineage is being tracked, it should be used to optimize other data-related business workflows, such as better error handling.

Automating Data Lineage & Its Impact on Data Management

I know we have talked about the benefits of automated lineage tracking over manual, but in reality, there isn’t any comparison between the two methods. The manual approach is only good for a healthy discussion, but in practice, organizations must always use automated approaches. This is because modern firms manage hundreds of databases, each containing hundreds of tables. Each of these tables goes under several transformations every day. A manual approach would require several resources and would not be feasible for large enterprises.

Automated data lineage tools track data, from all origins, throughout its journey. They extract metadata at each stage and mark the dataset accordingly. These markings are used to create a map of the data, and this entire visualization is available to the user for inspection. These also allow users to inspect the state of the dataset at each tag.

This automated data process brings great benefits to organizations. Since lineage is being tracked simultaneously, it can be used for business analysis and impact analysis without wasting any time. It can also be sued by data governance teams for constant monitoring of policies and data quality. Let’s take a look at some common data lineage tools.

Some Examples of Data Lineage Tools

  1. Dremio: Dremio is a data-centric company that helps organizations structure their data and workflows. It is trusted by several industry giants, including 3 of the Fortune 5. It exposes the data stored in the lake and its lineage to users, which can be queried normally and inspected at will.

  2. Octopai: Octopai is a tool built specifically for data lineage and discovery. They offer three different types of lineage, which are cross-system lineage, end-to-end column lineage, and inner system lineage.

  3. CloverDX (formerly CloverETL): CloverDX offers a variety of data-related solutions such as migration, warehousing, and quality management. They also offer easy deployments to popular clouds such as AWS and Azure.

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