About data profiling

Dataplex data profiling lets you identify common statistical characteristics of the columns in your BigQuery tables. This information helps you to understand and analyze your data more effectively.

Information like typical data values, data distribution, and null counts can accelerate analysis. When combined with data classification, data profiling can detect data classes or sensitive information that, in turn, can enable access control policies.

Dataplex also uses this information to recommend rules for data quality checks.

Conceptual model

Dataplex lets you better understand the profile of your data by creating a data profiling scan.

The following diagram shows how Dataplex scans data to report on statistical characteristics.

A data profiling scan analyzes table data to report on statistical characteristics.

A data profiling scan is associated with one BigQuery table and scans the table to generate the data profiling results. A data profiling scan supports several configuration options.

Configuration options

This section describes the configuration options available for running data profiling scans.

Scheduling options

You can schedule a data profiling scan with a defined frequency or on demand through the API or the Google Cloud console.

Scope

As part of the specification of a data profiling scan, you can specify the scope of a job as one of the following options:

  • Full table: The entire table is scanned in the data profiling scan. Sampling, row filters, and column filters are applied on the entire table before calculating the profiling statistics.

  • Incremental: Incremental data that you specify is scanned in the data profile scan. Specify a Date or Timestamp column in the table to be used as an increment. Typically, this is the column on which the table is partitioned. Sampling, row filters, and column filters are applied on the incremental data before calculating the profiling statistics.

Filter data

You can filter data to be scanned for profiling by using row filters and column filters. Using filters helps you reduce the execution time and cost, and exclude sensitive and unuseful data.

  • Row filters: Row filters let you focus on data within a specific time period or from a specific segment, such as region. For example, you can filter out data with a timestamp before a certain date.

  • Column filters: Column filters let you include and/or exclude specific columns from your table to run the data profiling scan.

Sample data

Dataplex lets you specify a percentage of records from your data to sample for running a data profiling scan. Creating data profiling scans on a smaller sample of data can reduce the execution time and cost of querying the entire dataset.

Multiple data profiling scans

Dataplex lets you create multiple data profiling scans at a time using the Google Cloud console. You can select up to 100 tables from one dataset and create a data profiling scan for each dataset. Learn more.

Export scan results to BigQuery table

You can export the data profiling scan results to a BigQuery table for further analysis. To customize reporting, you can connect the BigQuery table data to a Looker dashboard. You can build an aggregated report by using the same results table across multiple scans.

Data profiling results

The data profiling results include the following values:

Column type Data profiling results
Numeric column
  • Percentage of null values.
  • Percentage of approximate unique (distinct) values.
  • Top 10 most common values in the column. It can be less than 10 if the number of unique values in the column is less than 10 (null values aren't included). For each of these most common values, the percentage of their occurrence in the data scanned in the current scan is displayed.
  • Average, standard deviation, minimum, approximate lower quartile, approximate median, approximate upper quartile, and maximum values.
String column
  • Percentage of null values.
  • Percentage of approximate unique (distinct) values.
  • Top 10 most common values in the column, which can be less than 10 if the number of unique values in the column is less than 10.
  • Average, minimum, and maximum length of the string.
Other non-nested columns (date, time, timestamp, binary, etc.)
  • Percentage of null values.
  • Percentage of approximate unique (distinct) values.
  • Top 10 most common values in the column, which can be less than 10 if the number of unique values in the column is less than 10.
All other nested or complex data-type columns (such as Record, Array, JSON) or any column with repeated mode.
  • Percentage of null values.

The results include the number of records scanned in every execution.

Reporting and monitoring

You can monitor and analyze the data profiling results using the following reports and methods:

  • Reports published with the source table in the BigQuery and Data Catalog pages

    If you have configured a data profiling scan to publish the results in the BigQuery and Data Catalog pages in the Google Cloud console, then you can view the latest data profiling scan results in these pages in the Data Profile tab, from any project.

    Published reports.

  • Historical, per job report in Dataplex

    On the Dataplex Profile page, you can view the detailed reports for the latest and historical jobs. This includes column-level profile information and the configuration that was used.

    Historical per job report.

  • Analysis tab

    On the Dataplex Profile page, you can use the Analysis tab to view the trends for a given statistic of a column over multiple profile jobs. For example, if you have an incremental scan, you can view how the average of a value has been trending over time.

    Analysis tab.

  • Build your own dashboard or analytics

    If you have configured a data profiling scan to export or save results to a BigQuery table, then you can build your own dashboards using tools, such as Looker Studio.

Limitations

  • Data profiling results aren't published to Data Catalog as tags.
  • Data profiling is supported for BigQuery tables with all column types except BIGNUMERIC. A scan created for a table with a BIGNUMERIC column results in a validation error and isn't successfully created.
  • The BigQuery tables to be scanned must have 300 columns or less.

Pricing

  • Dataplex uses the premium processing SKU to charge for data profiling. For more information, see Pricing.

  • Publishing data profiling results to Data Catalog isn't yet available. When it becomes available, it will be charged at the same rate as Catalog metadata storage pricing. For more information, see Pricing.

  • Dataplex premium processing for data profiling is billed per second with a one-minute minimum.

  • You aren't charged for failed profiling scans.

  • 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.

  • 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 Dataplex premium processing SKU, use the label goog-dataplex-workload-type with value DATA_PROFILE.

  • To filter aggregate charges, use the following labels:

    • 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

What's next?