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No-Code Test Cases

Run data quality tests at the table, column, and dimension level across all supported database connectors. Tests range from business validations — confirming data meets real-world expectations — to technical checks like ensuring required columns are never null and identifier columns hold only unique values. No YAML or JSON config files needed — set up everything from the UI by selecting a test level, filling in the details, and clicking Create.

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How to Create a Test

1. Navigate to the desired table and click the Data Observability tab.
2. Click Add and select Test Case.
3. Select a test level and enter the test details. See the sections below for field details on each level:
   • Table Level
   • Column Level
   • Dimension Level (Beta)
   
4. Click Create to save and deploy the test.

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Table Level Test

A Table Level Test validates properties of the entire table — row counts, column structure, freshness, or custom SQL logic. To create a Table Level Test, enter the following details:

• Table: Select the table you want to test.
• Test Type: Choose the type of validation to run. Use the Custom Query shortcut on the right to write a SQL-based test directly.
• Name: Add a name that best defines your test case.
• Description: Describe what the test validates.
• Tags: Optionally tag the test for filtering and organization.
• Glossary Terms: Optionally link business glossary terms to add context.

Collate currently supports the following table level test types:

• Table Column Count to be Between: Define the Min. and Max.
• Table Column Count to Equal: Define a number.
• Table Column Name to Exist: Define a column name.
• Table Column Names to Match Set: Add comma-separated column names to match. You can also verify if the column names are in order.
• Custom SQL Query: Define a SQL expression. Select a strategy for Rows or Count, and define a threshold to determine if the test passes or fails.
• Table Row Count to be Between: Define the Min. and Max.
• Table Row Count to Equal: Define a number.
• Table Row Inserted Count to be Between: Define the Min. and Max. row count. Works with Timestamp, Date, and DateTime columns. Specify a range type (Hour, Day, Month, or Year) and an interval.
• Compare 2 Tables for Differences: Compare two tables to check for data integrity.
• Table Data to Be Fresh: Validate the freshness of a table’s data.

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Column Level Test

A Column Level Test validates specific column properties — null counts, value ranges, uniqueness, pattern matching, and more. To create a Column Level Test, enter the following details:

• Table: Select the table that contains the column you want to test.
• Column: Select the column to test. A summary of the column’s context appears on the right.
• Test Type: Choose the type of validation to run.
• Name: Add a name that best defines your test case.
• Description: Describe what the test validates.
• Tags: Optionally tag the test for filtering and organization.
• Glossary Terms: Optionally link business glossary terms to add context.

Collate currently supports the following column level test types:

• Column Value Lengths to be Between: Define the Min. and Max.
• Column Value Max. to be Between: Define the Min. and Max.
• Column Value Mean to be Between: Define the Min. and Max.
• Column Value Median to be Between: Define the Min. and Max.
• Column Value Min. to be Between: Define the Min. and Max.
• Column Values Missing Count: Define the number of missing values. Match all null and empty values as missing, and configure additional missing strings such as N/A.
• Column Values Sum to be Between: Define the Min. and Max.
• Column Value Std Dev to be Between: Define the Min. and Max.
• Column Values to be Between: Define the Min. and Max.
• Column Values to be in Set: Add an array of allowed values.
• Column Values to be Not in Set: Add an array of forbidden values.
• Column Values to be Not Null
• Column Values to be Unique
• Column Values to Match Regex Pattern: Define the regular expression that column entries should match.
• Column Values to Not Match Regex: Define the regular expression that column entries should not match.

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Dimension Level Test

A Dimension Level Test is a column test segmented by one or more dimension columns. Instead of a single pass/fail result for the whole column, you get separate results for each unique combination of dimension values — so you can pinpoint exactly which segment of your data is failing. To create a Dimension Level Test, enter the following details:

• Table: Select the table that contains the column you want to test.
• Column: Select the column to test.
• Dimensions: Select one or more columns to segment results by. Use low-cardinality columns such as region, status, or product_category for the most useful breakdown.
• Top Dimensions: Set how many top dimension combinations to display in the results (default: 5).
• Test Type: Choose from the same test types available for column-level tests.
• Name: Add a name that best defines your test case.
• Description: Describe the test case.

Example: If you test the amount column with region and product_type as dimensions, Collate returns separate results for each combination — North America / Electronics, Europe / Clothing, and so on — so you can immediately see which segment is causing a failure. For concepts, best practices, and real-world examples, see Dimensional Validation.

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View Test Results

Once you create a test, it appears in the Data Quality tab. Edit the Display Name and Description for any test directly from there.

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Set Up a Pipeline

Set up a pipeline to run tests at a regular interval. Follow the steps below:

1. Click the Pipeline tab as shown in the below image.
2. Click Add Pipeline.
3. Enter the pipeline details:
   • Name: Enter a name for the pipeline.
   • Scheduler for Test Cases: Choose how it runs:
     • Schedule: Runs automatically. Set the Every, Hour, and Minute fields to define the frequency — for example, every day at 12:00 AM.
     • On Demand: Run the tests manually whenever needed.
   • Enable Debug Log: Toggle on to capture detailed logs for troubleshooting.
   • Raise on Error: Toggle on to stop the pipeline if a test throws an error.
   • Select All Test Cases: Toggle on to include all tests, or search for specific ones in the Test Case field. Use the Status, Test Type, and Column filters to narrow the list.
   
4. Click Submit to save and activate the pipeline.

After the pipeline is scheduled, you can view and manage the pipeline in the Pipelines tab.

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Handle Test Failures

If a test fails, you can edit the test status to New, Acknowledged, or Resolved. To edit the status for a test, follow the steps below:

1. In the Status column, click the edit icon next to a failed test.
2. Select a test Status.
   Note: If you are marking the test status as Resolved, you must specify the Reason for the failure and add a Comment. The reasons for failure can be Duplicates, False Positive, Missing Data, Other, or Out of Bounds.
3. Click Submit to update the status.

Set up alerts to be notified when a test fails.