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Custom Assertions

Feature Availability
Self-Hosted DataHub
Managed DataHub

⚠️ The Custom Assertions feature is currently in private beta, part of the Acryl Observe module, and may only be available to a limited set of design partners.

If you are interested in trying it and providing feedback, please reach out to your Acryl Customer Success representative.

Introduction​

Can you remember a time when the meaning of Data Warehouse Table that you depended on fundamentally changed, with little or no notice? If the answer is yes, how did you find out? We'll take a guess - someone looking at an internal reporting dashboard or worse, a user using your your product, sounded an alarm when a number looked a bit out of the ordinary. Perhaps your table initially tracked purchases made on your company's e-commerce web store, but suddenly began to include purchases made through your company's new mobile app.

There are many reasons why an important Table on Snowflake, Redshift, or BigQuery may change in its meaning - application code bugs, new feature rollouts, changes to key metric definitions, etc. Often times, these changes break important assumptions made about the data used in building key downstream data products like reporting dashboards or data-driven product features.

What if you could reduce the time to detect these incidents, so that the people responsible for the data were made aware of data issues before anyone else? With Acryl DataHub Custom Assertions, you can.

Acryl DataHub allows users to define complex expectations about a particular warehouse Table through custom SQL queries, and then monitor those expectations over time as the table grows and changes.

In this article, we'll cover the basics of monitoring Custom Assertions - what they are, how to configure them, and more - so that you and your team can start building trust in your most important data assets.

Let's get started!

Support​

Custom Assertions are currently supported for:

  1. Snowflake
  2. Redshift
  3. BigQuery

Note that an Ingestion Source must be configured with the data platform of your choice in Acryl DataHub's Ingestion tab.

Note that SQL Assertions are not yet supported if you are connecting to your warehouse using the DataHub CLI or a Remote Ingestion Executor.

What is a Custom Assertion?​

A Custom Assertion is a highly configurable Data Quality rule used to monitor a Data Warehouse Table for unexpected or sudden changes in its meaning. Custom Assertions are defined through a raw SQL query that is evaluated against the Table. You have full control over the SQL query, and can use any SQL features supported by your Data Warehouse. Custom Assertions can be particularly useful when you have complex tables or relationships that are used to generate important metrics or reports, and where the meaning of the table is expected to be stable over time. If you have existing SQL queries that you already use to monitor your data, you may find that Custom Assertions are an easy way to port them to Acryl DataHub to get started.

For example, imagine that you have a Table that tracks the number of purchases made on your company's e-commerce web store. You have a SQL query that you use to calculate the number of purchases made in the past 24 hours, and you'd like to monitor this metric over time to ensure that it is always greater than 1000. You can use a Custom Assertion to do this!

Anatomy of a Custom Assertion​

At the most basic level, Custom Assertions consist of a few important parts:

  1. An Evaluation Schedule
  2. A Query
  3. An Condition Type
  4. An Assertion Description

In this section, we'll give an overview of each.

1. Evaluation Schedule​

The Evaluation Schedule: This defines how often to query the given warehouse Table. This should usually be configured to match the expected change frequency of the Table, although it can also be less frequently depending on the requirements. You can also specify specific days of the week, hours in the day, or even minutes in an hour.

2. Query​

The Query: This is the SQL query that will be used to evaluate the Table. The query should return a single row with a single column. Currently only numeric values are supported (integer and floats). The query can be as simple or as complex as you'd like, and can use any SQL features supported by your Data Warehouse. This requires that the configured user account has read access to the asset. Make sure to use the fully qualified name of the Table in your query.

Use the "Try it out" button to test your query and ensure that it returns a single row with a single column. The query will be run against the Table in the context of the configured user account, so ensure that the user has read access to the Table.

3. Condition Type​

The Condition Type: This defines the conditions under which the Assertion will fail. The list of supported operations is:

  • Is Equal To: The assertion will fail if the query result is equal to the configured value
  • Is Not Equal To: The assertion will fail if the query result is not equal to the configured value
  • Is Greater Than: The assertion will fail if the query result is greater than the configured value
  • Is Less Than: The assertion will fail if the query result is less than the configured value
  • Is outside a range: The assertion will fail if the query result is outside the configured range
  • Grows More Than: The assertion will fail if the query result grows more than the configured range. This can be either a percentage (Percentage) or a number (Value).
  • Grows Less Than: The assertion will fail if the query result grows less than the configured percentage. This can be either a percentage (Percentage) or a number (Value).
  • Growth is outside a range: The assertion will fail if the query result growth is outside the configured range. This can be either a percentage (Percentage) or a number (Value).

Custom Assertions also have an off switch: they can be started or stopped at any time with the click of button.

4. Assertion Description​

The Assertion Description: This is a human-readable description of the Assertion. It should be used to describe the meaning of the Assertion, and can be used to provide additional context to users who are viewing the Assertion.

Creating a Custom Assertion​

Prerequisites​

  1. Permissions: To create or delete Custom Assertions for a specific entity on DataHub, you'll need to be granted the Edit Assertions and Edit Monitors privileges for the entity. This is granted to Entity owners by default.

  2. Data Platform Connection: In order to create a Custom Assertion, you'll need to have an Ingestion Source configured to your Data Platform: Snowflake, BigQuery, or Redshift under the Integrations tab.

Once these are in place, you're ready to create your Custom Assertions!

Steps​

  1. Navigate to the Table you want to monitor
  2. Click the Validations tab

  1. Click + Create Assertion

  1. Choose Custom

  2. Configure the evaluation schedule. This is the frequency at which the assertion will be evaluated to produce a pass or fail result, and the times when the query will be executed.

  3. Provide a SQL query that will be used to evaluate the Table. The query should return a single row with a single column. Currently only numeric values are supported (integer and floats). The query can be as simple or as complex as you'd like, and can use any SQL features supported by your Data Warehouse. Make sure to use the fully qualified name of the Table in your query.

  1. Configure the evaluation condition type. This determines the cases in which the new assertion will fail when it is evaluated.

  1. Add a description for the assertion. This is a human-readable description of the Assertion. It should be used to describe the meaning of the Assertion, and can be used to provide additional context to users who are viewing the Assertion.

  1. (Optional) Use the Try it out button to test your query and ensure that it returns a single row with a single column, and passes the configured condition type.

  1. Click Next
  2. Configure actions that should be taken when the Custom Assertion passes or fails

  • Raise incident: Automatically raise a new DataHub Incident for the Table whenever the Custom Assertion is failing. This may indicate that the Table is unfit for consumption. Configure Slack Notifications under Settings to be notified when an incident is created due to an Assertion failure.
  • Resolve incident: Automatically resolved any incidents that were raised due to failures in this Custom Assertion. Note that any other incidents will not be impacted.
  1. Click Save.

And that's it! DataHub will now begin to monitor your Custom Assertion for the table.

To view the time of the next Custom Assertion evaluation, simply click Custom and then click on your new Assertion:

Once your assertion has run, you will begin to see Success or Failure status for the Table

Stopping a Custom Assertion​

In order to temporarily stop the evaluation of a Custom Assertion:

  1. Navigate to the Validations tab of the Table with the assertion
  2. Click Custom to open the Custom Assertions list
  3. Click the three-dot menu on the right side of the assertion you want to disable
  4. Click Stop

To resume the Custom Assertion, simply click Turn On.

Creating Custom Assertions via API​

Under the hood, Acryl DataHub implements Custom Assertion Monitoring using two "entity" concepts:

  • Assertion: The specific expectation for the custom assertion, e.g. "The table was changed in the past 7 hours" or "The table is changed on a schedule of every day by 8am". This is the "what".

  • Monitor: The process responsible for evaluating the Assertion on a given evaluation schedule and using specific mechanisms. This is the "how".

Note that to create or delete Assertions and Monitors for a specific entity on DataHub, you'll need the Edit Assertions and Edit Monitors privileges for it.

GraphQL​

In order to create a Custom Assertion that is being monitored on a specific Evaluation Schedule, you'll need to use 2 GraphQL mutation queries to create a Custom Assertion entity and create an Assertion Monitor entity responsible for evaluating it.

Start by creating the Custom Assertion entity using the createSqlAssertion query and hang on to the 'urn' field of the Assertion entity you get back. Then continue by creating a Monitor entity using the createAssertionMonitor.

Examples​

To create a Custom Assertion Entity that checks whether a query result is greater than 100:

mutation createSqlAssertion {
  createSqlAssertion(
    input: {
      entityUrn: "<urn of the table to be monitored>",
      type: METRIC,
      description: "<description of the custom assertion>",
      statement: "<SQL query to be evaluated>",
      operator: GREATER_THAN,
      parameters: {
        value: {
          value: "100",
          type: NUMBER
        }
      }
    }
  ) {
    urn
  }
}

The supported custom assertion types are METRIC and METRIC_CHANGE. If you choose METRIC_CHANGE, you will need to provide a changeType parameter with either ABSOLUTE or PERCENTAGE values. The supported operator types are EQUAL_TO, NOT_EQUAL_TO, GREATER_THAN, GREATER_THAN_OR_EQUAL_TO, LESS_THAN, LESS_THAN_OR_EQUAL_TO, and BETWEEN (requires minValue, maxValue). The supported parameter types are NUMBER.

To create an Assertion Monitor Entity that evaluates the custom assertion every 8 hours:

mutation createAssertionMonitor {
  createAssertionMonitor(
    input: {
      entityUrn: "<urn of entity being monitored>",
      assertionUrn: "<urn of assertion created in first query>",
      schedule: {
        cron: "0 */8 * * *",
        timezone: "America/Los_Angeles"
      },
      parameters: {
        type: DATASET_SQL
      }
    }
  ) {
    urn
  }
}

This entity defines when to run the check (Using CRON format - every 8th hour) and how to run the check (using the Information Schema).

After creating the monitor, the new assertion will start to be evaluated every 8 hours in your selected timezone.

You can delete assertions along with their monitors using GraphQL mutations: deleteAssertion and deleteMonitor.

Tips​

info

Authorization

Remember to always provide a DataHub Personal Access Token when calling the GraphQL API. To do so, just add the 'Authorization' header as follows:

Authorization: Bearer <personal-access-token>

Exploring GraphQL API

Also, remember that you can play with an interactive version of the Acryl GraphQL API at https://your-account-id.acryl.io/api/graphiql