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Compare data using SodaCL

Last modified on 27-Sep-23

There are several ways to use SodaCL metrics and checks to compare data across datasets and data sources. The following offers some advice about how and when to use different types of checks to obtain the comparison results you need.

See also: Reconciliation checks

Have you got an idea or example of how to compare data that we haven’t documented here? Let us know!

Compare data in the same data source and schema
Compare data in different data sources or schemas
Compare dates in a dataset to validate event sequence


Compare data in the same data source and schema

Use a cross check to conduct a row count comparison between datasets in the same data source.
If you wish to compare datasets in different data sources, or datasets in the same data source but with different schemas, see Compare data in different data sources or schemas.

checks for dim_employee:
  - row_count same as dim_department_group

Use a reference check to conduct a row-by-row comparison of values in two datasets in the same data source and return a result that indicates the volume and samples of mismatched rows, as in the following example which ensures that the values in each of the two names columns are identical.
If you wish to compare datasets in the same data source but with different schemas, see Compare data in different data sources or schemas.

checks for dim_customers_dev:
  - values in (last_name, first_name) must exist in dim_customers_prod (last_name, first_name)

Alternatively, you can use a failed rows check to customize a SQL query that compares the values of datasets.

- failed rows:
      name: Validate that the data is the same as retail customers
      fail query: |
                  with table_1_not_in_table_2 as (
                  select
                    *
                  from retail_customers
                  except
                  select
                    *
                  from retail_sfdc_customers
                  )
                  , table_2_not_in_table_1 as (
                  select
                    *
                  from retail_sfdc_customers
                  except
                  select
                    *
                  from retail_customers
                  )
                  select
                  'found in retail_sfdc_customers but missing in retail_customers' as directionality,
                  *
                  from table_1_not_in_table_2
                  union all
                  select
                  'found in retail_customers but missing in retail_sfdc_customers' as directionality,
                  *
                  from table_2_not_in_table_1


Compare data in different data sources or schemas

Use a cross check to conduct a simple row count comparison of datasets in two different data sources, as in the following example that compares the row counts of two datasets in different data sources.
Note that each data source involved in this check has been connected to data source either in the configuration.yml file with Soda Library, or in the Add Data Source workflow in Soda Cloud.

checks for dim_customer:
  - row_count same as dim_customer in aws_postgres_retail

You can use a reference check to compare the values of different datasets in the same data source (same data source, same schema), but if the datasets are in different schemas, as might happen when you have different environments like production, staging, development, etc., then Soda considers those datasets as different data sources. Where that is the case, you have a couple of options.

You can use a cross check to compare the row count of datasets in the same data source, but with different schemas. First, you must add dataset + schema as a separate data source connection in your configuration.yml, as in the following example that uses the same connection details but provides different schemas:

data_source retail_customers_stage:
  type: postgres
  connection:
    host: location.eu-west-1.rds.amazonaws.com
    username: ${USER}
    password: ${PASS}
    database: postgres
  schema: staging

data_source retail_customers_prod:
  type: postgres
  connection:
    host: location.eu-west-1.rds.amazonaws.com
    username: ${USER}
    password: ${PASS}
    database: postgres
  schema: production

Then, you can define a cross check that compares values across these data sources.

checks for dim_customer:
# Check row count between datasets in different data sources
  - row_count same as retail_customers_stage in retail_customers_prod

Alternatively, depending on the type of data source you are using, you can use a failed rows check to write a custom SQL query that compares contents of datasets that you define by adding the schema before the dataset name, such as prod.retail_customers and staging.retail_customers.

The following example accesses a single Snowflake data source and compares values between the same datasets but in different databases and schemas: prod.staging.dmds_scores and prod.measurement.post_scores.

- failed rows:
    fail query: |
                WITH src as (
              SELECT src_page_id, src_post_id
                 FROM prod.staging.dmds_scores
              ), tgt as (
              SELECT page_id, post_id, partition_date FROM prod.measurement.post_scores
              )
              SELECT src_page_id, src_post_id
                  FROM src
                  LEFT JOIN tgt
                  ON src.src_page_id = tgt.page_id AND src.src_post_id = tgt.post_id
                  WHERE (src.src_page_id IS NOT NULL AND src.src_post_id IS NOT NULL) 
                  AND (tgt.page_id IS NULL AND tgt.post_id IS NULL)

See also: Configure the same scan to run in multiple environments

Compare dates in a dataset to validate event sequence

You can use a user-defined metric to write a custom SQL query that compares date values in the same dataset.
Refer to Custom check templates.

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Documentation always applies to the latest version of Soda products
Last modified on 27-Sep-23