Effective AI systems operate on a foundation of context and continuous trust. When you use Dataplex Universal Catalog, Google Cloud’s unified data governance platform, the metadata that describes your data is no longer static — it’s where your AI applications can go to know where to find data and what to trust.
But when you have complex data pipelines, it’s easy for your data’s journey to become obscured, making it difficult to trace information from its origin to its eventual impact. To solve this, we are extending Dataplex lineage capabilities from object-level to column-level, starting with support for BigQuery.
“To power our AI strategy, we need absolute trust in our data. Column-level lineage provides that. It’s the foundation for governing…
Effective AI systems operate on a foundation of context and continuous trust. When you use Dataplex Universal Catalog, Google Cloud’s unified data governance platform, the metadata that describes your data is no longer static — it’s where your AI applications can go to know where to find data and what to trust.
But when you have complex data pipelines, it’s easy for your data’s journey to become obscured, making it difficult to trace information from its origin to its eventual impact. To solve this, we are extending Dataplex lineage capabilities from object-level to column-level, starting with support for BigQuery.
“To power our AI strategy, we need absolute trust in our data. Column-level lineage provides that. It’s the foundation for governing our data responsibly and confidently.” - Latheef Syed - AVP, Data & AI Governance Engineering at Verizon
While object-level lineage tracks the top-level connections between entire tables, column-level lineage charts the specific, granular path of a single data column as it moves and transforms. With that, we are now providing a dynamic and granular map to govern your data-to-AI ecosystem, so you can ground your agentic AI applications in context. Lineage is upgraded to Column-level at no extra cost.
Answering critical questions about your data
Data professionals often need precise answers about the complex relationships in their BigQuery datasets. Column-level lineage provides a graph of data flows that you can trace to find these answers quickly. Now you can:
Confirm that a column used in your AI models originates from an authoritative source
Understand how changes to one column affect other columns downstream before you make a modification
Trace the root cause of an issue with a column by examining its upstream transformations
Verify that sensitive data at the column level is used correctly throughout your organization
“Column-level lineage takes the trusted map of our data ecosystem to the next level. It’s the precision tool we need to fully understand the impact of a change, trace a problem to its source, and ensure compliance down to the most granular detail.” - Arvind Rajagopalan - AVP, Data / AI & Product Engineering at Verizon
Explore lineage visually
Dataplex now provides an interactive, visual representation of column-level lineage relationships. You can select a single column in a table to see a graph of all its upstream and downstream connections. As you navigate the graph at the asset level, you can drill down to the column level to verify which specific columns are affected by a process. You can also visualize the direct lineage paths between the columns of two different assets, giving you a focused view of their relationship.
Column-level tracing for AI models
Tables used for AI and ML model training often have data coming from different sources and taking different paths, and it’s important to have granular visibility into the data’s journey. For example, in complex AI/ML feature tables, a single table for model training may contain many columns. Column-level lineage can verify that the one column originates from a trusted, audited financial system, while another one comes from ephemeral web logs. Table-level lineage would obscure this critical distinction, treating all features with the same level of trust.
Powering context-aware AI agents
More companies are developing AI agents to automate tasks and answer complex questions about their data, and these agents require a deep understanding of the business and organizational context to be effective. The granular metadata provided by column-level lineage supplies this necessary context. For example, it can allow the agent to distinguish between similarly named metrics. Tracing each column’s path, including its frequency of usage, and freshness, it gives context to the agent on the importance of a column if affected by a change, or severity of impact when troubleshooting. By grounding AI agents in a rich, factual map of your data assets and their relationships, you can build more accurate and reliable agentic workflows.
Get started
You can start using column-level lineage for BigQuery today in Dataplex.
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