Version: Next

DataHub Open Data Quality Assertions Specification

DataHub is developing an open-source Data Quality Assertions Specification & Compiler that will allow you to declare data quality checks / expectations / assertions using a simple, universal YAML-based format, and then compile this into artifacts that can be registered or directly executed by 3rd party Data Quality tools like Snowflake DMFs, dbt tests, Great Expectations or Acryl Cloud natively.

Ultimately, our goal is to provide an framework-agnostic, highly-portable format for defining Data Quality checks, making it seamless to swap out the underlying assertion engine without service disruption for end consumers of the results of these data quality checks in catalogging tools like DataHub.

Integrations

Currently, the DataHub Open Assertions Specification supports the following integrations:

Snowflake DMF Assertions

And is looking for contributions to build out support for the following integrations:

[Looking for Contributions] dbt tests
[Looking for Contributions] Great Expectation checks

Below, we'll look at how to define assertions in YAML, and then provide an usage overview for each support integration.

The Specification: Declaring Data Quality Assertions in YAML

The following assertion types are currently supported by the DataHub YAML Assertion spec:

Each assertion type aims to validate a different aspect of structured table (e.g. on a data warehouse or data lake), from structure to size to column integrity to custom metrics.

In this section, we'll go over examples of defining each.

Freshness Assertions

Freshness Assertions allow you to verify that your data was updated within the expected timeframe. Below you'll find examples of defining different types of freshness assertions via YAML.

Validating that Table is Updated Every 6 Hours

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: freshness
    lookback_interval: '6 hours'
    last_modified_field: updated_at
    schedule:
      type: interval
      interval: '6 hours' # Run every 6 hours

This assertion checks that the purchase_events table in the test_db.public schema was updated within the last 6 hours by issuing a Query to the table which validates determines whether an update was made using the updated_at column in the past 6 hours. To use this check, we must specify the field that contains the last modified timestamp of a given row.

The lookback_interval field is used to specify the "lookback window" for the assertion, whereas the schedule field is used to specify how often the assertion should be run. This allows you to schedule the assertion to run at a different frequency than the lookback window, for example to detect stale data as soon as it becomes "stale" by inspecting it more frequently.

Supported Source Types

Currently, the only supported sourceType for Freshness Assertions is LAST_MODIFIED_FIELD. In the future, we may support additional source types, such as HIGH_WATERMARK, along with data source-specific types such as AUDIT_LOG and INFORMATION_SCHEMA.

Volume Assertions

Volume Assertions allow you to verify that the number of records in your dataset meets your expectations. Below you'll find examples of defining different types of volume assertions via YAML.

Validating that Tale Row Count is in Expected Range

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: volume
    metric: 'row_count'
    condition:
      type: between
      min: 1000
      max: 10000
    # filters: "event_type = 'purchase'" Optionally add filters. 
    schedule:
      type: on_table_change # Run when new data is added to the table. 

This assertion checks that the purchase_events table in the test_db.public schema has between 1000 and 10000 records. Using the condition field, you can specify the type of comparison to be made, and the min and max fields to specify the range of values to compare against. Using the filters field, you can optionally specify a SQL WHERE clause to filter the records being counted. Using the schedule field you can specify when the assertion should be run, either on a fixed schedule or when new data is added to the table. The only metric currently supported is row_count.

Validating that Table Row Count is Less Than Value

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: volume
    metric: 'row_count'
    condition:
      type: less_than_or_equal_to
      value: 1000
    # filters: "event_type = 'purchase'" Optionally add filters. 
    schedule:
      type: on_table_change # Run when new data is added to the table. 

Validating that Table Row Count is Greater Than Value

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: volume
    metric: 'row_count'
    condition:
      type: greater_than_or_equal_to
      value: 1000
    # filters: "event_type = 'purchase'" Optionally add filters. 
    schedule:
      type: on_table_change # Run when new data is added to the table. 

Supported Conditions

The full set of supported volume assertion conditions include:

equal_to
not_equal_to
greater_than
greater_than_or_equal_to
less_than
less_than_or_equal_to
between

Column Assertions

Column Assertions allow you to verify that the values in a column meet your expectations. Below you'll find examples of defining different types of column assertions via YAML.

The specification currently supports 2 types of Column Assertions:

Field Value: Asserts that the values in a column meet a specific condition.
Field Metric: Asserts that a specific metric aggregated across the values in a column meet a specific condition.

We'll go over examples of each below.

Field Values Assertion: Validating that All Column Values are In Expected Range

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: amount
    condition:
        type: between
        min: 0
        max: 10
    exclude_nulls: True
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    # failure_threshold:
    #  type: count
    #  value: 10
    schedule:
      type: on_table_change

This assertion checks that all values for the amount column in the purchase_events table in the test_db.public schema have values between 0 and 10. Using the field field, you can specify the column to be asserted on, and using the condition field, you can specify the type of comparison to be made, and the min and max fields to specify the range of values to compare against. Using the schedule field you can specify when the assertion should be run, either on a fixed schedule or when new data is added to the table. Using the filters field, you can optionally specify a SQL WHERE clause to filter the records being counted. Using the exclude_nulls field, you can specify whether to exclude NULL values from the assertion, meaning that NULL will simply be ignored if encountered, as opposed to failing the check. Using the failure_threshold, we can set a threshold for the number of rows that can fail the assertion before the assertion is considered failed.

Field Values Assertion: Validating that All Column Values are In Expected Set

The validate a VARCHAR / STRING column that should contain one of a set of values:

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: product_id
    condition:
        type: in
        value: 
          - 'product_1'
          - 'product_2'
          - 'product_3'
    exclude_nulls: False
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    # failure_threshold:
    #  type: count
    #  value: 10
    schedule:
      type: on_table_change

Field Values Assertion: Validating that All Column Values are Email Addresses

The validate a string column contains valid email addresses:

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: email_address
    condition:
      type: matches_regex
      value: "[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}"
    exclude_nulls: False
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    # failure_threshold:
    #  type: count
    #  value: 10
    schedule:
      type: on_table_change

Field Values Assertion: Supported Conditions

The full set of supported field value conditions include:

in
not_in
is_null
is_not_null
equal_to
not_equal_to
greater_than # Numeric Only
greater_than_or_equal_to # Numeric Only
less_than # Numeric Only
less_than_or_equal_to # Numeric Only
between # Numeric Only
matches_regex # String Only
not_empty # String Only
length_greater_than # String Only
length_less_than # String Only
length_between # String Only

Field Metric Assertion: Validating No Missing Values in Column

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: col_date
    metric: null_count
    condition:
      type: equal_to
      value: 0
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    schedule:
      type: on_table_change

This assertion ensures that the col_date column in the purchase_events table in the test_db.public schema has no NULL values.

Field Metric Assertion: Validating No Duplicates in Column

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: id
    metric: unique_percentage
    condition:
      type: equal_to
      value: 100
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    schedule:
      type: on_table_change

This assertion ensures that the id column in the purchase_events table in the test_db.public schema has no duplicates, by checking that the unique percentage is 100%.

Field Metric Assertion: Validating String Column is Never Empty String

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: field
    field: name
    metric: empty_percentage
    condition:
      type: equal_to
      value: 0
    # filters: "event_type = 'purchase'" Optionally add filters for Column Assertion. 
    schedule:
      type: on_table_change

This assertion ensures that the name column in the purchase_events table in the test_db.public schema is never empty, by checking that the empty percentage is 0%.

Field Metric Assertion: Supported Metrics

The full set of supported field metrics include:

null_count
null_percentage
unique_count
unique_percentage
empty_count
empty_percentage
min
max
mean
median
stddev
negative_count
negative_percentage
zero_count
zero_percentage

Field Metric Assertion: Supported Conditions

The full set of supported field metric conditions include:

equal_to
not_equal_to
greater_than
greater_than_or_equal_to
less_than
less_than_or_equal_to
between

Custom SQL Assertions

Custom SQL Assertions allow you to define custom SQL queries to verify your data meets your expectations. The only condition is that the SQL query must return a single value, which will be compared against the expected value. Below you'll find examples of defining different types of custom SQL assertions via YAML.

SQL Assertions are useful for more complex data quality checks that can't be easily expressed using the other assertion types, and can be used to assert on custom metrics, complex aggregations, cross-table integrity checks (JOINS) or any other SQL-based data quality check.

Validating Foreign Key Integrity

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: sql
    statement: |
      SELECT COUNT(*)
      FROM test_db.public.purchase_events AS pe
      LEFT JOIN test_db.public.products AS p
      ON pe.product_id = p.id
      WHERE p.id IS NULL
    condition:
      type: equal_to
      value: 0
    schedule:
      type: interval
      interval: '6 hours' # Run every 6 hours

This assertion checks that the purchase_events table in the test_db.public schema has no rows where the product_id column does not have a corresponding id in the products table.

Comparing Row Counts Across Multiple Tables

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: sql
    statement: |
      SELECT COUNT(*) FROM test_db.public.purchase_events
      - (SELECT COUNT(*) FROM test_db.public.purchase_events_raw) AS row_count_difference
    condition:
      type: equal_to
      value: 0
    schedule:
      type: interval
      interval: '6 hours' # Run every 6 hours

This assertion checks that the number of rows in the purchase_events exactly matches the number of rows in an upstream purchase_events_raw table by subtracting the row count of the raw table from the row count of the processed table.

Supported Conditions

The full set of supported custom SQL assertion conditions include:

equal_to
not_equal_to
greater_than
greater_than_or_equal_to
less_than
less_than_or_equal_to
between

Schema Assertions (Coming Soon)

Schema Assertions allow you to define custom SQL queries to verify your data meets your expectations. Below you'll find examples of defining different types of custom SQL assertions via YAML.

The specification currently supports 2 types of Schema Assertions:

Exact Match: Asserts that the schema of a table - column names and their data types - exactly matches an expected schema
Contains Match (Subset): Asserts that the schema of a table - column names and their data types - is a subset of an expected schema

Validating Actual Schema Exactly Equals Expected Schema

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: schema
    condition: 
      type: exact_match 
      columns:
      - name: id
        type: INTEGER
      - name: product_id
        type: STRING
      - name: amount
        type: DECIMAL
      - name: updated_at
        type: TIMESTAMP
    schedule:
      type: interval
      interval: '6 hours' # Run every 6 hours

This assertion checks that the purchase_events table in the test_db.public schema has the exact schema as specified, with the exact column names and data types.

Validating Actual Schema is Contains all of Expected Schema

version: 1
assertions:
  - entity: urn:li:dataset:(urn:li:dataPlatform:snowflake,test_db.public.purchase_events,PROD)
    type: schema
    condition: 
      type: contains
      columns:
      - name: id
        type: integer
      - name: product_id
        type: string
      - name: amount
        type: number
    schedule:
      type: interval
      interval: '6 hours' # Run every 6 hours

This assertion checks that the purchase_events table in the test_db.public schema contains all of the columns specified in the expected schema, with the exact column names and data types. The actual schema can also contain additional columns not specified in the expected schema.

Supported Data Types

The following high-level data types are currently supported by the Schema Assertion spec:

string
number
boolean
date
timestamp
struct
array
map
union
bytes
enum

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DataHub Open Data Quality Assertions Specification

Integrations​

The Specification: Declaring Data Quality Assertions in YAML​

Freshness Assertions​

Validating that Table is Updated Every 6 Hours​

Supported Source Types​

Volume Assertions​

Validating that Tale Row Count is in Expected Range​

Validating that Table Row Count is Less Than Value​

Validating that Table Row Count is Greater Than Value​

Supported Conditions​

Column Assertions​

Field Values Assertion: Validating that All Column Values are In Expected Range​

Field Values Assertion: Validating that All Column Values are In Expected Set​

Field Values Assertion: Validating that All Column Values are Email Addresses​

Field Values Assertion: Supported Conditions​

Field Metric Assertion: Validating No Missing Values in Column​

Field Metric Assertion: Validating No Duplicates in Column​

Field Metric Assertion: Validating String Column is Never Empty String​

Field Metric Assertion: Supported Metrics​

Field Metric Assertion: Supported Conditions​

Custom SQL Assertions​

Validating Foreign Key Integrity​

Comparing Row Counts Across Multiple Tables​

Supported Conditions​

Schema Assertions (Coming Soon)​

Validating Actual Schema Exactly Equals Expected Schema​

Validating Actual Schema is Contains all of Expected Schema​

Supported Data Types​

Integrations

The Specification: Declaring Data Quality Assertions in YAML

Freshness Assertions

Validating that Table is Updated Every 6 Hours

Supported Source Types

Volume Assertions

Validating that Tale Row Count is in Expected Range

Validating that Table Row Count is Less Than Value

Validating that Table Row Count is Greater Than Value

Supported Conditions

Column Assertions

Field Values Assertion: Validating that All Column Values are In Expected Range

Field Values Assertion: Validating that All Column Values are In Expected Set

Field Values Assertion: Validating that All Column Values are Email Addresses

Field Values Assertion: Supported Conditions

Field Metric Assertion: Validating No Missing Values in Column

Field Metric Assertion: Validating No Duplicates in Column

Field Metric Assertion: Validating String Column is Never Empty String

Field Metric Assertion: Supported Metrics

Field Metric Assertion: Supported Conditions

Custom SQL Assertions

Validating Foreign Key Integrity

Comparing Row Counts Across Multiple Tables

Supported Conditions

Schema Assertions (Coming Soon)

Validating Actual Schema Exactly Equals Expected Schema

Validating Actual Schema is Contains all of Expected Schema

Supported Data Types