The data lineage can be defined as the GPS information of the data. It shows the experts the path of the data and its transformations. Recording how data is processed, changed, and transmitted, data lineage enables companies to gain meaningful insights into how they conduct their businesses.
Data lineage visualizes the journey of a data flow from its source to its destination. Many organizations use data visualization software with data lineage to track their data and provide real-time information to users.
What is data lineage?
Data lineage is a process. It identifies the source of the data, records its changes and movements over time, and visualizes the flow from the source to the end-user. Data lineage, which gives data scientists visibility into data dynamics, also facilitates the identification of root causes of problems.
Data lineage reveals the patterns and causes of data change. It helps organizations track bugs, perform system migrations, approximate data discovery, and metadata, and implement process changes with less risk.
Strategic business decisions depend on data accuracy. Tracking and verifying data processes becomes difficult without a good data lineage. It allows users to visualize the full flow of information from source to destination, making detecting and correcting anomalies easy. With data lineage, users can replay certain parts or inputs of the data stream to debug or generate lost output.
Users who don’t need technical background details use the data source to get a comprehensive overview of the data flow. Many database systems leverage the data source to address debugging and validation challenges.
Data lineage and data governance
Data governance is the set of rules and procedures that organizations use to protect and control data. Data linage is important in data governance as it informs how data flows from source to destination.
Businesses use data layers that differ according to their needs. Lower levels of data lineage provide a simple visual representation of how data flows within an organization without including specific details about the transformations that occur as data moves. The highest tier offers insights into how data flow can be optimized and ways to improve data platforms. Organizations choose a data lineage tier based on their governance structure, costs incurred in implementation and monitoring, regulatory regulations, and business impact.
Understanding data lineage is a critical requirement of metadata management. Therefore, it is important for data warehouse and data lake managers. Metadata management allows you to view the data flow in various systems, making it easy to find all the data associated with a particular report or extract, transform, and load (ETL) process.
Benefits of data lineage
In addition to helping troubleshoot problems or perform system migrations relatively easily, data lineage also allows you to ensure the confidentiality and integrity of data by tracking changes to the data, how they are performed, and who made them.
By referring to data lineage, IT teams can visualize the data journey from start to finish. Visualization makes it easier for IT professionals and gives administrators the confidence to make on-the-spot decisions.
Data lineage tools help answer the following questions:
- How and by what process was the data changed?
- Who was responsible for data changes?
- When was the change made?
- What was the geographic location of the person making the changes?
- Why was the change made, and what was the context?
The organization’s goals determine the requirements for a data lineage system. It benefits organizations in the following areas:
- Strategic decision-making: Enterprise users can better understand how processed data transforms by seeing it. This data can improve business operations, products, and services.
- Maximum benefit from new and legacy datasets: It allows businesses to monitor changing datasets with evolving data collection techniques and technologies.
- Data migration: It helps IT teams understand the location and lifecycle of data sources to quickly move data to a new storage location, making migration projects less risky.
- Data governance: It provides granular visibility across the data lifecycle, helping businesses manage risks, comply with industry regulations, and perform internal audits.
Use cases of data lineage
Data lineage helps organizations track data flow, see dependencies, and understand transformations throughout the lifecycle. Teams take advantage of the detailed view of the data flow for the following purposes.
Identifying root causes of problems
For example, there is confusion when sales figures do not match the finance department’s records; in such cases, it is difficult to pinpoint where the actual error lineage is dated. Data lineage provides a plausible explanation for such situations. Business intelligence (BI) managers can use it to monitor the entire data flow and see changes made during processing.
Administrators can also refer to data lineage to provide a reasonable explanation for any situation, whether there is an error. If there is an error, teams can correct the error at the source, ensuring that end-user data is uniform across the entire organization.
System upgrades
When migrating to a new enterprise system, it is important to understand which datasets are relevant and which have become obsolete or unavailable. Data lineage helps you know the data you use to run business operations and limits the expenditure on storing and managing irrelevant data.
You can seamlessly plan and execute system migrations and updates with data lineage. It helps you visualize data sources, dependencies, and processes, so you understand exactly what data you need to move.
Impact analysis
Identifying affected reports, data items, and end-users is important before implementing a change. Data lineage allows teams to visualize downstream data streams, measure the impact of change, and see how business users interact with data and how a change will affect them. Businesses can use data lineage to see the impact of a particular change before it happens.
Data lineage techniques
Organizations may use a few basic methods to establish data lineage on important data. These procedures ensure that every data change or the processing is documented, allowing you to trace data elements throughout the lifecycle of information assets as they move through processes.
Metadata is gathered and stored after each data transformation, which is subsequently used to generate a data lineage representation.
Lineage by parsing
By analyzing one of the most sophisticated lineage models that read data logic, we’ve discovered that it can read a large portion of the source code. Reverse engineering data transformation logic provides complete end-to-end traceability.
Because it requires a thorough grasp of all tools and coding languages employed to transform and analyze data, lineage by parsing is difficult to put into practice. This may include ETL logic, SQL-based, JAVA solutions, extensible markup language (XML) solutions, legacy data formats, and more.
It’s difficult to build a data lineage solution that supports various programming languages and tools that enable dynamic processing, making it even more complicated. While selecting a solution, ensure it handles input parameters, runtime information, and default values while parsing all these elements to automate end-to-end data lineage delivery.
Pattern-based lineage
Pattern-based lineage, in contrast to code-based lineage, uses patterns to provide a representation of the lineages instead of reading any code. Pattern-based lineage takes advantage of metadata about tables, reports, and columns and profiles them to produce a lineage based on similarities and trends.
In this approach, you have the advantage of monitoring data rather than algorithms. Your data lineage solution does not need to understand programming languages and tools used to analyze data. It may be implemented similarly across any database system, including Oracle or MySQL. However, this technique does not always provide accurate results compared to laser rods or CD cases. Many elements, such as transformation logic, are not disclosed.
When the source code is inaccessible or unavailable, this technique may be used to maintain data lineage when it’s impossible to comprehend programming logic.
Self-contained lineage
A self-contained lineage tracks every data movement and transformation within an all-inclusive environment that includes data processing logic, master data management, and more. It’s simple to keep track of the circulation of data and its lifecycle.
However, the self-contained solution remains isolated to one particular setting and is oblivious to everything else. The self-contained data lineage solution may fail to provide the anticipated outcomes as new demands arise and data processing tools are employed.
Lineage by data tagging
A transformation engine labels every item of data that moves or changes in terms of lineage. All tags are read from beginning to end to generate a lineage representation. Although it appears to be a powerful data lineage method, it only works if there is adequate control over data movement via a consistent transformation engine or tool.
This approach eliminates data movements outside the transformation engine, making it ideal for validating closed data systems. This may not be the most effective data lineage technique in some circumstances. Developers avoid creating formal data columns to the solution model at each touchpoint when dealing with data changes.
Blockchain may be one answer to lineage by data tagging issues, but it hasn’t achieved widespread use enough to change businesses’ data lifecycle significantly.
Manual lineage
The process of tracing a lineage is similar to that of tracing a family tree. Interviews with individuals who understand the company’s data flow are required. You may speak with application owners, data integration experts, data stewards, and others involved in the data lifecycle. Using spreadsheets, you may trace your family’s lineage using straightforward mappings.
You may miss out on an interview or encounter conflicting facts, resulting in faulty data lineage. While examining the code, you’ll have to compare columns and tables manually while verifying symbols and other information. Despite these issues, the complexity of dynamically expanding code volume and growth adds to the difficulties of manual data lineage. When code is unavailable or inaccessible, this approach can be beneficial in determining what’s going on in a system. Manual data lineage is also effective when code is not available or inaccessible.
