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Everything You Need to Know About Data Lineage

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What is data lineage? What are the characteristics of data lineage? What are the uses of data lineage? What are the methods of data lineage collection? If you are looking for answers to the above questions, you have come to the right place.

In this article, we will go into detail about everything you need to know about data lineage.

What is Data lineage?

In the entire process of data generation, processing and integration, circulation, and final demise, a relationship will naturally form between data. This relationship between data is expressed by drawing on a similar relationship in human society, which is called the data lineage relationship. Data lineage is one of the components of metadata.

Data Lineage

Data Lineage 

It can be used to analyze the lineage path of the table and fields from the data source to the current table, whether the relationship between the lineage fields is satisfied, the data consistency of the concern, and the rationality of the table design. It can be used to analyze the impact of changes in upstream data on downstream data and trace the source of upstream problems when changes in downstream data occur.

Why Businesses Use Data Lineage

Businesses leverage data lineage to gain transparency and traceability in their data ecosystems, understanding the origin, flow, and transformation of data across various systems. Data lineage provides a comprehensive map of how data moves through an organization, from its source to its final destination, capturing every step of processing in between. This visibility is crucial for ensuring data quality, compliance, and governance. By using data lineage, businesses can enhance decision-making, troubleshoot issues more efficiently, meet regulatory requirements, and build trust in their data infrastructure. It facilitates a holistic understanding of data, promoting informed strategies and fostering a data-driven culture within the organization.

Data Lineage Characteristics

  1. Attribution: Generally speaking, specific data belongs to a specific organization or individual, and the data has attribution.
  2. Multisource: The same data can have multiple sources. One data can be generated by processing multiple data, and this processing process can be multiple.
  3. Traceability: The data lineage relationship reflects the life cycle of data, and reflects the entire process of data from generation to demise, with traceability.
  4. Hierarchical: Data lineage relationships are hierarchical. The description information of the data, such as the classification, induction, and summary of the data, forms new data, and the description information of different degrees forms the level of the data.

Data Lineage Techniques

Data lineage techniques play a crucial role in understanding and managing the flow of data within an organization. Here are explanations of four data lineage techniques:

Pattern-Based Lineage

Pattern-based lineage involves recognizing and establishing relationships in data by identifying specific patterns or structures within the data. This technique relies on predefined rules or algorithms to infer the lineage based on recognized patterns. For example, in a financial dataset, a pattern-based lineage technique might identify specific transaction codes or formats to trace the flow of financial transactions through various systems.

Lineage by Data Tagging

Lineage by data tagging involves attaching metadata tags to the data elements as they move through different stages of processing. Each tag carries information about the data’s source, transformation, and destination. This technique provides a clear and explicit way to track the lineage of individual data elements. For instance, in a customer relationship management (CRM) system, each customer’s data might be tagged with information about its origin and any subsequent modifications.

Self-Contained Lineage

Self-contained lineage involves embedding lineage information directly within the data or its accompanying documentation. Data elements carry their lineage details, including their source, transformations, and dependencies. This approach ensures that the data itself contains the necessary information for understanding its lineage. For instance, a dataset might include metadata fields specifying the origin and processing history of each data element.

Lineage by Parsing

Lineage by parsing involves analyzing the metadata or documentation associated with data processes to extract lineage information. This technique parses through code, logs, or documentation to identify relationships between different data elements. For example, parsing SQL queries or ETL (Extract, Transform, Load) scripts can reveal how data is transformed and moved within a database or data warehouse.

The choice of technique depends on factors such as the complexity of data transformations, the need for automation, and the level of granularity required for lineage tracking.

How Data Lineage Works

Data Lineage provides a detailed understanding of how data flows through different stages, systems, and transformations in the company, it tracks all the movement of the data from its creation or acquisition to its consumption. Here we will see how the data lineage works in steps. 

  • Data Source Identification: At first we identify the source of data from where the data is originating. This includes databases, external files system, any API, or any other data repository.
  • Data Capturing: Once the data source is identified at the next step we capture data using ETL(Extract, Load, Transform), data replications, APIs, or other data integration Methods.  
  • Lineage Annotation: As the data source is identified, captured, and processed through different paths and stages. Lineage information is attached to each element of the data. This metadata includes details such as the data’s source, extraction, or transformation processes applied to the data. Also, intermediate storage locations and destination systems of the data are attached.  
  • Transformation and Movement Tracking of the data – Data Lineage tracks all the movements of data elements. This records the changes made at different locations in the company across the data pipeline.
  • Dependency Mapping – It captures dependence between different data elements and processes. It identifies relationships such as parent-child relationships, data dependencies, and data lineage hierarchies.
  • Visualization and Exploration – All the tracking data records are presented using graphs such as diagrams, graphs, or interactive interfaces.

Data Lineage Use Cases

Data Traceability: The data lineage relationship reflects the ins and outs of the data, which can help us track the source of the data and track the data processing process. When data is abnormal, it is necessary to trace the cause of the abnormality and control the risk to an appropriate level.

Especially with the development of enterprises, there are many data sources and different levels of quality, which will inevitably lead to an impact on the data results. Therefore, the ability of data traceability is very valuable.

Impact Analysis: With more and more data applications, the data flow chain is getting longer and longer. For changes in the core business of a source, downstream analysis applications must be kept in sync. Without impact analysis, abnormal access will be caused to each data service. This is often a core requirement of the development sector to facilitate impact assessments.

Data Value: How to measure the value of data is a very difficult problem. To evaluate the value of data, you need a basis. The data lineage provides reference data in several aspects to facilitate evaluation. Data audience: The more data demanders, the greater the data value.

Update frequency: The more frequent the update, the greater the value of the data.

Quality Assessment: This is the requirement of data analysis applications, and it is necessary to control the quality of the final result data. When data problems are found, it is necessary to further trace the data source, analyze the causes of quality problems, and then solve them. From the data lineage diagram, the data processing process can be easily obtained, and then the possible quality problems can be analyzed.

The Life Cycle: Through the data lineage, the entire life cycle of the data can be intuitively obtained. For less valuable data, consider tiering, archiving, or even destruction.

Compliance Requirements: This is often the need of upper management. For regulatory compliance, all points and sources of data flow are the key points that need to be regulated. Therefore, a comprehensive understanding of the relationship between data is required.

Conclusion

In summary, data lineage is a vital aspect of metadata, providing a hierarchical, multisource, and traceable representation of data relationships. Understanding and implementing data lineage is crucial for optimizing data management and ensuring its quality and value throughout its lifecycle.

What is Data Lineage- FAQs

What is lineage in ETL?

In ETL (Extract, Transform, Load), lineage refers to the tracking of data’s origin, transformations, and destination throughout the entire ETL process, ensuring data quality and traceability.

What is an example of a lineage?

In a data warehouse, the lineage of a sales report could include the source (sales transactions), transformations (aggregation), and destination (sales report table).

What are the two types of data lineage?

  • Forward Lineage: Tracks the flow of data from its source to its destination.
  • Backward Lineage: Traces data from its destination back to its source, understanding how it’s utilized.

What is data lineage in SQL?

In SQL, data lineage involves mapping how data moves through queries and transformations. It tracks the relationships between tables, columns, and views in a database.

How do I start data lineage?

Begin data lineage by understanding your data sources, documenting transformations, and utilizing tools or metadata repositories to record and visualize the flow of data through your systems.

What is called lineage?

Lineage, in the context of data, refers to the comprehensive documentation of a data element’s journey from its origin, through various transformations, to its final destination, ensuring traceability and data quality.



Last Updated : 25 Jan, 2024
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