Data lineage maps the data journey from origin to destination, and provides insights about the various steps the data goes through along that journey. Organizations need visibility into data lineage to make data-driven decisions with full context of the data they’re using.
As data explodes in velocity, variety and veracity, it is important to track the lineage of the data — how it is transformed. Data lineage answers questions such as:
Many businesses struggle to find and understand their enterprise data. To gain valuable insights from data, they must discover it and understand its context. To do this, they use metadata.
Metadata is the data about the data. This includes definitions of technical, business and operations data. Metadata management, data lineage and data provenance all use metadata to provide insights and context about enterprise data.
- Metadata management is the practice of managing metadata for enterprise use.
- Data lineage consists of metadata for tables, columns, and business reports — with an end-to-end map of the data journey.
- Data provenance refers to the source of the data – where the data originated. Data provenance is metadata that shows details of the origin, changes to, and details supporting the confidence or validity of data.
Benefits of Data Lineage
Understanding the provenance and lineage of data sources is valuable for several reasons. These include:
Data lineage helps you evaluate the trustworthiness of data based on its provenance.
Audit and Compliance
Data lineage provides audit trails for data governance and regulatory purposes and reduces the cost of compliance with existing and future regulation.
Data lineage helps you migrate data to the cloud by providing context that enables impact analysis. With data lineage, you can plan your migration and ensure that there’s no impact on downstream data consumers.
Visibility for Data Users
Data lineage provides visibility for self-service analysis by business users – which includes visibility into how data moves and transforms through the data pipelines, business reports, business intelligence dashboards.
Data lineage offers better quality data for better analysis and business results and helps avoid data duplication. It can also help you understand and correct data errors.
Data lineage can help you build an agile data infrastructure that is responsive to change and reduces the cost of application maintenance and new development.
Next-Generation Data Lineage
Data lineage becomes critical in data lakes, which store massive data volumes, a variety of data types, and rapidly changing data. Data engineers and data architects need metadata management tools to manage data lakes and enable self-service access. A next-generation metadata management solution creates a business-friendly semantic layer on top of physical data in the data lake, enabling data consumers to view data lineage and interact with data in the lake without having to rely on IT.
For example, Dremio’s semantic layer exposes a business representation of an organization’s data assets so that it can be accessed using common business terms. This semantic layer creates an abstraction layer so that business users can interact with the objects created in it without worrying about the complexities of where and how the data is physically stored and organized. Ideally, a metadata management solution should provide capabilities such as a standard interface to query data in the data lake, access control, data masking for sensitive information, data encryption, and auditing to track the integrity of data assets and track lineage throughout the data lifecycle.
For a deeper dive into Data Lineage, check out our advanced guide What Is Data Lineage?