Data lineage refers to the process of tracing the movement of data from its origin and the different stages of transformation it has to go through over a period of time before reaching its final destination.
It provides a comprehensive view of the data source, its transformations, and its final destination within the data pipeline.
Tools used for data lineage offer a comprehensive account of the entire lifecycle of data, encompassing details about its source and any changes that were made to it during ETL or ELT process.
This capability helps organization to understand data context, various processes around data processing and hence determine the data quality .
Data lineage is not a new concept , but now the advancement in technology and automation has made it scalable and accessible .
Earlier , tracking a data lineage required lot of manual work. The process required understanding data resources, racing their origins to the points of intake, creating a record of those origins, outlining the route that the data took as it flowed through multiple pipelines and stages of modification and identifying the data consumer applications.
Modern tech stack has now made this process much more automated and less time consuming.
As the tech landscape and environment gets complex with multiple data sources, consumers and data processing stages, the task of tracking lineage becomes more difficult.
However with the right strategy data lineage can be tracked and can prove to be immensely beneficial
Lineage can be categorized into coarse-grained lineage and fine-grained lineage.
Coarse-grained lineage, provides a high-level overview of the data flow, without capturing every single transformation or operation. It typically focuses on the major milestones in the data processing pipeline, such as when data was initially ingested, when it was transformed into a particular format, and when it was loaded into a particular database or application.
It is useful for understanding the overall flow of data through a system and for identifying key dependencies between different data elements.
Fine-grained lineage provides a detailed record of every transformation and operation performed on a piece of data, from its original source to its final state. This includes information about each step in the data processing pipeline, including which data sources were used, what transformations were applied, and which users or applications interacted with the data.
It is useful for understanding exactly how a piece of data was created and how it was transformed over time, which can be helpful for auditing and troubleshooting.
Data lineage documents the relationship between enterprise data in various business and IT applications.
Data lineage involves :
Tracing data sources and stake holders.
Defining data lineage requirements
Setting up right tools to capture the relevant information
Implementing data lineage processes
Continuously monitoring and improving the process and tools.
Here are some common use cases for data lineage:
Compliance: Data lineage is essential for compliance with regulations and data governance policies. It helps organizations demonstrate that they are using data in a responsible and ethical manner by tracking data access and use.
Data Quality: Data lineage helps organizations ensure data quality by enabling them to trace errors back to their source. This helps organizations quickly identify and correct data quality issues.
Data Integration: Data lineage helps organizations integrate data from different sources by providing a clear understanding of the data’s origin, format, and transformations. This enables organizations to map data relationships and identify potential data integration challenges.
Data Analytics: Data lineage is critical for data analytics, as it helps data scientists and analysts understand the data’s history and how it has been transformed. This enables them to make more informed decisions and draw accurate conclusions from the data.
Change Management: Data lineage helps organizations manage change by tracking how data changes over time. This enables organizations to monitor the impact of changes on data and ensure that data remains accurate and reliable.
Overall, data lineage is a critical tool for managing data effectively and efficiently in today’s data-driven world.