As of April 10, 2026, Dataplex Universal Catalog is now called Knowledge Catalog. The API, client library, CLI, and IAM names remain unchanged. For more information, see Introducing the Google Cloud Knowledge Catalog.

Knowledge Catalog FAQ

This document answers frequently asked questions about Google Cloud Knowledge Catalog (formerly Dataplex Universal Catalog).

For more information about Knowledge Catalog, see Knowledge Catalog overview.

General concepts

Explore frequently asked questions about Knowledge Catalog features.

What is Knowledge Catalog?

Knowledge Catalog is an intelligent governance solution for data and AI assets in Google Cloud. It provides a centralized inventory where you can discover, manage, and govern your data across Google Cloud data sources like BigQuery, Cloud Storage, Pub/Sub, and Spanner.

Knowledge Catalog uses AI to automate data discovery, metadata enrichment, and data quality. Through its governed data catalog, Knowledge Catalog provides the essential grounding that AI agents need to generate high-quality content.

What is Knowledge Catalog used for?

Knowledge Catalog solves the "data cold start" problem—the time wasted trying to find, understand, and trust data before you can actually use it. Its primary uses include the following:

Accelerated data discovery: Instead of navigating complex organizational silos to locate data, you can use natural language search (for example, "Show me the most recent customer churn data") to find assets across Google Cloud resources instantly, which increases productivity for data consumers.
Grounding AI agents: It acts as the "source of truth" for generative AI or ADK. By linking physical data to business definitions, it ensures that AI agents (like those built on Vertex AI) use high-quality data, which significantly reduces AI hallucinations and improves trust in AI-generated insights.
Automated data governance: It automatically scans your data to identify sensitive information (that is, PII), tracks where data comes from (lineage), and monitors its accuracy (automatic data quality). These capabilities help improve data trust, security, and compliance with less manual effort.
Discovering "dark data": It can scan unstructured files (for example, PDFs or images in Cloud Storage), extract the information inside, and make it searchable and queryable in BigQuery, which helps you unlock insights from previously inaccessible data.

To get started with these scenarios, see Knowledge Catalog use cases.

What types of metadata does Knowledge Catalog store?

Knowledge Catalog stores three types of metadata:

Technical metadata: Automatically harvested schemas, table names, and system properties.
Business metadata: User-defined context such as business descriptions, glossary terms, and ownership.
Runtime metadata: Information about data lineage, data quality scores, and data profiling statistics.

Data insights billing

Explore frequently asked questions about token-based billing, usage metering, and administrative controls for the data insights feature in Knowledge Catalog.

When will I start being billed for using data insights?

Active billing for data insights starts on October 27, 2026.

How is data insights usage billed?

Usage is measured in data tokens. Data tokens represent the basic units of text and metadata processed by the Gemini model. These tokens are divided into two categories:

Input tokens: The context, instructions, and history sent to the model.
Output tokens: The generated natural language descriptions, relationships, and queries returned by the model.

For more information, see Data Cloud Agent pricing.

Do service accounts receive a free quota for data insights?

No. Service accounts don't receive a free per-user quota allowance. However, their usage is metered in the same way as standard users.

How can I prevent automatic charging for data insights?

To avoid incurring charges, verify your ongoing scans and stop triggering new scans. Administrators can also turn off the data insights capability by disabling the Dataplex API in the respective project.

How can I find out how much I am going to be charged for data insights?

Google Cloud provides metering for data tokens. This metering lets administrators and agent owners monitor their token consumption. To track token consumption for data insights scan jobs and build custom dashboards in Cloud Monitoring, see Monitor data insights token usage.

How can I control costs and set token limits for data insights?

To manage costs and set limits on data insights token usage, see Quotas and limits for data insights.

What happens when data insights quota limits are reached?

Knowledge Catalog checks both input and output token quotas before starting a scan and deducts the consumed tokens after a scan completes.

What happens to scans already in progress when the limit is crossed?

Knowledge Catalog checks quota only at the start of a scan, not mid-scan. If a scan starts while quota is still available and its token usage pushes the project over the daily limit, that scan completes normally and publishes its generated insights. After the post-scan billing reconciliation completes, Knowledge Catalog marks the daily quota bucket as exhausted (100%) for the remainder of the day.

What happens to subsequent scans triggered after the limit is reached?

After the input or output daily token quota is exhausted, Knowledge Catalog immediately blocks any new on-demand or scheduled insights scan before execution. Rejected scans don't incur token charges.

What do I see in the Google Cloud console?

When a scan fails because the token quota is exceeded, BigQuery Studio and Knowledge Catalog in the Google Cloud console display the following behavior:

On a table or dataset with existing insights: regeneration fails with the following inline error banner:

Knowledge Catalog Insights token quota limit exceeded for this project.

All previously generated descriptions and suggested SQL queries remain visible and usable.
On a new table or dataset with no prior insights: generation fails and displays the quota-exceeded error banner in the empty state. The Generate insights button remains available to retry.

How can I resume data insights generation?

To resume data insights generation, do one of the following:

Wait for the daily reset: daily token counters automatically reset to 0 at midnight Pacific Time (00:00 PT).
Increase the quota limit: a project administrator can raise the daily limit (or reset it to unlimited) on the Quotas & System Limits page in the Google Cloud console to resume insights generation immediately.

Billing and cost monitoring

Explore frequently asked questions about monitoring and attributing compute costs in Knowledge Catalog.

How do I monitor and attribute Knowledge Catalog DCU costs for data quality, data profiling, and data lineage?

Knowledge Catalog DataScan compute (auto data quality and data profiling) and data lineage Data Compute Units (DCUs) run on serverless compute in Google-managed infrastructure and don't publish time-series metrics under dataplex.googleapis.com/* to Cloud Monitoring.

To monitor, alert on, and attribute Knowledge Catalog DCU costs across projects and scans, export your Cloud Billing data to BigQuery and query system labels attached to Knowledge Catalog billing records:

For data quality and data profiling scans, use the goog-dataplex-datascan-* labels (such as goog-dataplex-datascan-id, goog-dataplex-datascan-data-source-project, and goog-dataplex-datascan-data-source-bigquery-table).
For Data Lineage processing, use the goog-dataplex-workload-type label with value LINEAGE.

For detailed label definitions and example BigQuery SQL queries, see Monitor and attribute Knowledge Catalog DCU costs with Cloud Billing export.