# Data Lineage: What Is It and What's It Used For Now? > Data lineage tracks the origin and transformations of every field in the pipeline. How to use it for governance, debugging, and compliance. Source: https://www.erathos.com/en/blog/what-is-data-lineage-and-what-is-it-for Em português: https://www.erathos.com/blog/what-is-data-lineage-and-what-is-it-for Published: 2024-10-15 Category: Governance & Observability ![Data lineage diagram showing data flow tracking from source to the analytics destination](https://cms-media.erathos.com/Vm7DehcbCXFFM4MQoCKBivZyTkg-1.png) ## Have you ever heard of data lineage? Data lineage is the process of tracking the flow of data over time, allowing for a clear understanding of where data originated, how it changed, and its final destination within the data pipeline. Reliable data is essential for driving better decision-making and process improvement across all business areas. However, this information is only valuable if stakeholders are confident in its accuracy, considering that insights are only as good as the quality of the data. Data lineage tools provide a detailed history of everything that happened to the information during its lifecycle, including transformations that occurred during ETL and ELT processes, data migrations, system updates, errors, and much more. Tracking data is crucial for ensuring the quality control of the information consumed and used in decision-making, allowing for consistency and accuracy validations. In addition, data lineage is a major step toward achieving data observability and agility in resolving errors, since you can observe the execution history to find the root cause of the problem. These tools go hand-in-hand with data governance goals, where visibility acts as a confirmation of data efficiency and quality. ## Debugging in practice: tracking a wrong metric back to its source A common scenario illustrates the real value of data lineage perfectly. A revenue dashboard shows a number that doesn't match expectations. Without lineage, the data team manually investigates: they review the query, check the dbt model, look at the raw data, and try to remember if anything changed recently. This process can take hours, or even days, depending on the complexity of the pipeline. With data lineage implemented, the path is straightforward: you start from the incorrect metric, trace the lineage up to find exactly which table feeds that field, what transformation was applied, and which original source the data came from. If a field's data type changed in the source three weeks ago, or if a sync partially failed in a specific window, the lineage points to this directly, instead of requiring manual, field-by-field investigation. This difference, between reconstructing the data's path from memory and simply querying a logged history, is what separates a data team that resolves incidents in minutes from one that takes days. ## Compliance and auditing: proving where the data came from Beyond debugging, data lineage plays a direct role in regulatory compliance. When a company needs to demonstrate to an auditor or to meet CCPA/GDPR requirements where a specific piece of data came from, who had access to it, and what transformations it underwent to get to the final report, the answer can't be "we trust that it's correct." It has to be traceable. This is particularly critical for sensitive data: customer information, financial data, or any field subject to deletion or anonymization requests under privacy laws. Without lineage, proving that data was correctly removed from every place it propagated to is a manual, error-prone task. With lineage, it's a simple query. ## How does it work? As we saw earlier, data lineage tools allow users to fully understand how data flows through the data pipeline. This happens through metadata. Metadata is "data about data," which includes various details about _data assets_, such as type, format, structure, author, creation date, modification date, and file size. Data lineage tools provide a complete view of metadata to guide users in determining which data is relevant to each goal. In recent years, the way we store and use data has evolved with the rise of big data. Companies are investing more in data science to drive smarter decision-making and better business outcomes. However, to build a robust analysis, you need to use data lineage tools and data catalogs to perform data mapping. While data lineage tools show the evolution of data over time using metadata, a data catalog uses the same information to create a searchable inventory of all _data assets_ in an organization. Together, they allow data professionals to understand the impact of different datasets on specific outcomes. ## How Erathos delivers part of this at the ingestion layer End-to-end data lineage, from the source to the final report, involves multiple layers of the data stack: ingestion, transformation, and modeling. Specifically at the ingestion layer, where Erathos operates, every sync generates an audit trail: when each record arrived, from which source, if there was a retry, and the complete execution history per connector. This audit trail is what we detail in [Data Governance](/blog/governanca-de-dados), and it is the first link in the lineage chain: without knowing exactly when and how data entered your warehouse, any lineage built in the downstream layers (transformation, modeling) remains incomplete. ## Frequently asked questions about data lineage **What is the difference between data lineage and a data catalog?** Data lineage shows the path and transformations a specific piece of data went through over time. A data catalog is a searchable inventory of all the organization's data assets, leveraging the same metadata, but focusing on discovery rather than tracing provenance. **Is data lineage only for large companies?** No. The value is clear at any scale as soon as you have more than one data source and more than one transformation between the source and the final report, which happens early on for most companies, not just enterprise ones. **Do I need a dedicated data lineage tool?** It depends on the complexity of your stack. Transformation tools like dbt already generate part of the lineage automatically (which models depend on which). The ingestion layer, covered by platforms like Erathos, complements this by logging where and when each piece of data entered the warehouse. **Does data lineage help with data privacy compliance?** Yes. It allows you to prove where a specific piece of data came from, what transformations it underwent, and trace all the places it propagated to, which is essential for reliably answering deletion requests or audit inquiries. ## Conclusion Data lineage turns the question "where did this number come from?" from a time-consuming manual investigation into a direct query. This is invaluable both for day-to-day debugging and regulatory compliance, and it all starts at the ingestion layer, where every sync must leave a clear trail of when and how the data arrived. [Create your free Erathos account](https://app.erathos.com/signup?slug=blog&button=cta&utm_campaign=data_lineage) and get a complete audit trail from the very first layer of your data pipeline.