Knowledge Catalog pricing is based on pay-as-you-go usage. Knowledge Catalog currently charges based on the following SKUs:

• Knowledge Catalog processing (standard and premium)
• Metadata storage

The following is a high-level overview of how each key Knowledge Catalog capability is billed:

Knowledge Catalog standard and premium processing are metered by the Data Compute Unit (DCU). DCU-hour is an abstract billing unit for Knowledge Catalog and the actual metering depends on the individual features you use.

Knowledge Catalog free tier

As part of the Google Cloud Free Tier, Knowledge Catalog offers some resources free of charge up to a specific limit. These free usage limits are available during and after the free trial period. If you go over these usage limits and are no longer in the free trial period, you will be charged according to the pricing as described in the sections above.

Note: The Knowledge Catalog free tier is only available for the Knowledge Catalog Standard Processing SKU, and is not available for the Knowledge Catalog Premium Processing SKU.

DCU charges for each feature are calculated as follows:

1. Auto data quality scans:

• The DCU-hour consumption is proportional to the processing involved in profiling the data and computing the data quality metrics. This is billed per second, with a minimum of one minute.
• The charge depends on the number of rows, the number of columns, the amount of data that you've scanned, the data quality rule configuration, the partitioning and clustering settings on the table, and the frequency of the scan.
• For data quality anomaly detection scans, DCU charges do not apply. Instead, standard BigQuery pricing for compute, storage, and BQML model training, processing, and deploying apply.
• Using a custom execution identity changes how you are billed for the scan. When you specify a custom execution identity, the compute and storage costs associated with the scan are billed directly to your BigQuery project, bypassing the standard Knowledge Catalog Premium SKUs.

2. There are several options to reduce the cost of auto data quality scans:

• Sampling
• Incremental scans
• To separate data quality charges from other charges in the Knowledge Catalog premium processing SKU, on the Cloud Billing report, use the label goog-dataplex-workload-type with value DATA_QUALITY.

3. To filter aggregate charges, use the following labels available in billing export in BigQuery:

• goog-dataplex-datascan-data-source-dataplex-entity
• goog-dataplex-datascan-data-source-dataplex-lake
• goog-dataplex-datascan-data-source-dataplex-zone
• goog-dataplex-datascan-data-source-project
• goog-dataplex-datascan-data-source-region
• goog-dataplex-datascan-id
• goog-dataplex-datascan-job-id

4. Data Profiling scans:

• The DCU-hour consumption is proportional to the processing involved in profiling the data and computing the data quality metrics. This is billed per second, with a minimum of one minute.
• The charge depends on the number of rows, numbers of columns, the amount of data scanned, partitioning and clustering settings on the table, and the frequency of the scan.
• Using a custom execution identity changes how you are billed for the scan. When you specify a custom execution identity, the compute and storage costs associated with the scan are billed directly to your BigQuery project, bypassing the standard Knowledge Catalog Premium SKUs.

5. There are several options to reduce the cost of data profiling scans:

• Sampling
• Incremental scans
• Column filtering
• Row filtering
• To separate data profiling charges from other charges in the Knowledge Catalog premium processing SKU, on the Cloud Billing report, use the label goog-dataplex-workload-type with value DATA_PROFILE.

6. To filter aggregate charges, use the following labels available in billing export in BigQuery:

• goog-dataplex-datascan-data-source-dataplex-entity
• goog-dataplex-datascan-data-source-dataplex-lake
• goog-dataplex-datascan-data-source-dataplex-zone
• goog-dataplex-datascan-data-source-project
• goog-dataplex-datascan-data-source-region
• goog-dataplex-datascan-id
• goog-dataplex-datascan-job-id

7. Data Lineage:

• The DCU-hour consumption is proportional to the processing involved to automatically parse lineage.
• To separate data lineage charges from other charges in the Knowledge Catalog premium processing SKU, on the Cloud Billing report, use the label goog-dataplex-workload-type with value LINEAGE.
• If you call the Data Lineage API Origin sourceType with a value other than CUSTOM, it causes additional costs.

Data lineage pricing example

User A enables data lineage to track lineage for BigQuery in their project. The project is in the us-central1 location. During one month, data lineage consumes 100 DCU-hours of Knowledge Catalog Premium processing, and generates 1GiB of data lineage metadata. The cost is:

As users interact with the Knowledge Catalog, API calls are free of charge, including:

• Creating and managing Knowledge Catalog resources
• Creating and managing lineage resources
• Catalog search

This section provides examples of how to calculate the Knowledge Catalog cost.

Small aspects

• User A creates and applies small aspects (1024 bytes each). For $10 per month, the user can store 5 GiB of metadata, which corresponds approximately to 5M aspects. Assuming one aspect per table, this amounts to a total of 5M tables with aspects.
• User B creates 5M aspects of 1 KB each on the 10th of the month, and deletes the aspects on the 20th. The cost is $3.33, calculated as 5 GiB of data divided by one-third month:

Large aspects

• User C creates and applies large aspects (10 KB each). For $10 per month, the user can store 5 GiB of metadata, which corresponds to approximately 500k aspects. Assuming one aspect per table, it amounts to a total of 500k tables with aspects.
• User D creates 10k aspect types (for example ETL, data governance, data quality), and applies large aspects (10 KB each) using each of the 10 aspect types. For $10 per month, the user can store 5 GiB of metadata, which corresponds approximately to 500k aspects. Assuming 10 aspects per table, it amounts to a total of 50k tables with aspects.

This section describes the pricing for Data Catalog, which is in deprecation phase (migration to the Knowledge Catalog in progress). For more information about the differences between Knowledge Catalog and Data Catalog, see Knowledge Catalog versus Data Catalog.

Data Catalog charges apply to metadata storage for Data Catalog and API calls made to the Data Catalog API.

Metadata storage and API call charges accrue daily. You can view unbilled usage on the Google Cloud console.

Note: Pricing models apply to accounts, not projects, unless specified otherwise.

Data Catalog API calls are billed as described in the following table:

Note: Search queries performed on the Data Catalog page in the Google Cloud console are offered at no charge.