description: This page provides an overview of working with DataHub SQL Assertions
---
import FeatureAvailability from '@site/src/components/FeatureAvailability';
# Custom Assertions
<FeatureAvailabilitysaasOnly/>
> ⚠️ 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!
5. 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.
6. 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.
8. 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.
9. (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.
