How can you design a software system for data lineage?

1 Define your goals and scope

The first step is to define your goals and scope for data lineage. What are the business and technical requirements that you need to meet? Who are the stakeholders and users of the data lineage information? How granular and comprehensive do you want your data lineage to be? For example, do you need to track data at the column, row, or cell level? Do you need to capture the logic, code, and metadata of each data transformation? Do you need to include external data sources and sinks? These questions will help you narrow down your scope and prioritize your efforts.

2 Choose your data lineage model

The next step is to choose your data lineage model. There are two main types of data lineage models: technical and business. Technical data lineage focuses on the low-level details of how data is moved and manipulated in the system. It shows the data sources, destinations, processes, and transformations that occur along the data pipeline. Business data lineage focuses on the high-level meaning and value of the data for the business. It shows the data entities, attributes, relationships, and rules that define the business logic and context of the data. Depending on your goals and scope, you may need to use one or both types of data lineage models.

3 Design your data lineage architecture

The third step is to design your data lineage architecture. This involves deciding how to collect, store, and access the data lineage information. There are three common approaches to collect data lineage information: passive, active, and hybrid. Passive collection involves extracting the data lineage information from existing sources, such as logs, metadata repositories, or data catalogs. Active collection involves injecting the data lineage information into the data pipeline, such as using custom code, annotations, or tags. Hybrid collection involves using a combination of passive and active methods. The choice of collection method depends on the availability, accuracy, and complexity of the data lineage information.

To store the data lineage information, you need to design a data lineage repository that can handle the volume, variety, and velocity of the data. The data lineage repository should be able to store both structured and unstructured data, such as schemas, scripts, queries, documents, etc. It should also be able to support different levels of granularity, such as batch, streaming, or event-based data. The data lineage repository should also be scalable, reliable, and secure.

To access the data lineage information, you need to design a data lineage interface that can provide a clear and intuitive visualization of the data flow and dependencies. The data lineage interface should be able to support different types of queries, such as forward, backward, or impact analysis. It should also be able to support different types of users, such as developers, analysts, or auditors. The data lineage interface should also be interactive, customizable, and collaborative.

4 Implement and test your data lineage system

The fourth step is to implement and test your data lineage system. This involves developing, deploying, and integrating the components of your data lineage architecture. You need to ensure that the data lineage collection, storage, and access methods are consistent, accurate, and efficient. You also need to ensure that the data lineage system is compatible and compliant with the existing data sources, processes, and standards in your system. You also need to test the functionality, performance, and usability of your data lineage system. You need to verify that the data lineage information is complete, correct, and current. You also need to validate that the data lineage system meets the expectations and needs of the stakeholders and users.

5 Monitor and maintain your data lineage system

The final step is to monitor and maintain your data lineage system. This involves keeping track of the health, quality, and usage of your data lineage system. You need to monitor the data lineage system for any errors, anomalies, or changes that may affect the data flow and dependencies. You also need to monitor the data quality and integrity of the data lineage information. You also need to monitor the user feedback and satisfaction of the data lineage system. You need to maintain the data lineage system by updating, improving, and enhancing the data lineage collection, storage, and access methods. You also need to maintain the data lineage repository by cleaning, archiving, and securing the data lineage information. You also need to maintain the data lineage interface by adding, modifying, or removing the data lineage features and functions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?