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: