Update: Data Lineage is now generally available on AWS and Azure.
We are excited to announce that data lineage for Unity Catalog, the unified governance solution for all data and AI assets on lakehouse, is now available in preview.
This blog will discuss the importance of data lineage, some of the common use cases, our vision for better data transparency and data understanding with data lineage, and a sneak peek into some of the data provenance and governance features we’re building.
What is data lineage and why is it important?
Data lineage describes the transformations and refinements of data from source to insight. Lineage includes capturing all the relevant metadata and events associated with the data in its lifecycle, including the source of the data set, what other data sets were used to create it, who created it and when, what transformations were performed, what other data sets leverage it, and many other events and attributes. With a data lineage solution, data teams get an end-to-end view of how data is transformed and how it flows across their data estate.
As more and more organizations embrace a data-driven culture and set up processes and tools to democratize and scale data and AI, data lineage is becoming an essential pillar of a pragmatic data management and governance strategy.
To understand the importance of data lineage, we have highlighted some of the common use cases we have heard from our customers below.
Data goes through multiple updates or revisions over its lifecycle, and understanding the potential impact of any data changes on downstream consumers becomes important from a risk management standpoint. With data lineage, data teams can see all the downstream consumers — applications, dashboards, machine learning models or data sets, etc. — impacted by data changes, understand the severity of the impact, and notify the relevant stakeholders. Lineage also helps IT teams proactively communicate data migrations to the appropriate teams, ensuring business continuity.
Data understanding and transparency
Organizations deal with an influx of data from multiple sources, and building a better understanding of the context around data is paramount to ensure the trustworthiness of the data. Data lineage is a powerful tool that enables data leaders to drive better transparency and understanding of data in their organizations. Data lineage also empowers data consumers such as data scientists, data engineers and data analysts to be context-aware as they perform analyses, resulting in better quality outcomes. Finally, data stewards can see which data sets are no longer accessed or have become obsolete to retire unnecessary data and ensure data quality for end business users .
Debugging and diagnostics
You can have all the checks and balances in place, but something will eventually break. Data lineage helps data teams perform a root cause analysis of any errors in their data pipelines, applications, dashboards, machine learning models, etc. by tracing the error to its source. This significantly reduces the debugging time, saving days, or in many cases, months of manual effort.
Compliance and audit readiness
Many compliance regulations, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPPA), Basel Committee on Banking Supervision (BCBS) 239, and Sarbanes-Oxley Act (SOX), require organizations to have clear understanding and visibility of data flow. As a result, data traceability becomes a key requirement in order for their data architecture to meet legal regulations. Data lineage helps organizations be compliant and audit-ready, thereby alleviating the operational overhead of manually creating the trails of data flows for audit reporting purposes.
Effortless transparency and proactive control with data lineage
The lakehouse provides a pragmatic data management architecture that substantially simplifies enterprise data infrastructure and accelerates innovation by unifying your data warehousing and AI use cases on a single platform. We believe data lineage is a key enabler of better data transparency and data understanding in your lakehouse, surfacing the relationships between data, jobs, and consumers, and helping organizations move toward proactive data management practices. For example:
- As the owner of a dashboard, do you want to be notified next time that a table your dashboard depends upon wasn’t loaded correctly?
- As a machine learning practitioner developing a model, do you want to be alerted that a critical feature in your model will be deprecated soon?
- As a governance admin, do you want to automatically control access to data based on its provenance?
All of these capabilities rely upon the automatic collection of data lineage across all use cases and personas — which is why the lakehouse and data lineage are a powerful combination.
Here are some of the features we are shipping in the preview:
Data lineage for tables
Data lineage for table columns
Data Lineage for notebooks, workflows, dashboards
- Built-in security: Lineage graphs in Unity Catalog are privilege-aware and share the same permission model as Unity Catalog. If users do not have access to a table, they will not be able to explore the lineage associated with the table, adding an additional layer of security for privacy considerations.
- Easily exportable via REST API: Lineage can be visualized in the Data Explorer in near real-time, and retrieved via REST API to support integrations with our catalog partners.
Getting started with data lineage in Unity Catalog
Data lineage is available with Databricks Premium and Enterprise tiers for no additional cost. If you already are a Databricks customer, follow the data lineage guides (AWS | Azure) to get started. If you are not an existing Databricks customer, sign up for a free trial with a Premium or Enterprise workspace.