# Demos

## Getting Started

![](img/advanced_validation.png)

Advanced Validation

A validation with a comprehensive set of rules.

![](img/starter_validation.png)

Starter Validation

A validation with the basics.

## Results And Reporting

![](img/data_extracts.png)

Data Extracts

Pulling out data extracts that highlight rows with validation failures.

![](img/step_report_column_values.png)

Step Reports for Column Data Checks

A step report for column checks shows what went wrong.

![](img/step_report_column_schema.png)

Step Report for a Schema Check

When a schema doesn't match, a step report gives you the details.

![](img/sundered_data.png)

Sundered Data

Splitting your data into 'pass' and 'fail' subsets.

## Actions And Thresholds

Set Failure Threshold Levels

Set threshold levels to better gauge adverse data quality.

![](img/validation_with_actions.png)

Step-Level Actions

Configure actions to trigger when validation thresholds are exceeded, such as logging warnings or errors.

![](img/validation_with_final_actions.png)

Final Actions

Execute actions after validation completes, such as sending alerts or generating summary reports.

## Column Value Checks

Apply Validation Rules to Multiple Columns

Create multiple validation steps by using a list of column names with columns=.

Checks for Missing Values

Perform validations that check whether missing/NA/Null values are present.

Custom Expression for Checking Column Values

A column expression can be used to check column values. Just use col_vals_expr() for this.

Comparison Checks Across Columns

Perform comparisons of values in columns to values in other columns.

Custom Validation with specially()

Create bespoke validations using specially() to implement domain-specific business rules.

Expect No Duplicate Rows

We can check for duplicate rows in the table with rows_distinct().

Checking for Duplicate Values

To check for duplicate values down a column, use rows_distinct() with a columns_subset= value.

Expectations with a Text Pattern

With col_vals_regex(), check for conformance to a regular expression.

Numeric Comparisons

Perform comparisons of values in columns to fixed values.

Set Membership

Perform validations that check whether values are part of a set (or not part of one).

## Advanced Topics

Validating Data Freshness

Use date-based validations to ensure your data is current and recent.

Verifying Row and Column Counts

Check the dimensions of the table with the \*\_count_match() validation methods.

CLI Interactive Demos

These CLI demos showcase practical data quality workflows that you can use!

Column Selector Functions: Easily Pick Columns

Use column selector functions in the columns= argument to conveniently choose columns.

Date and Datetime Validations

Comprehensive examples of date, datetime, and timezone-aware datetime comparisons.

Mutate the Table in a Validation Step

For far more specialized validations, modify the table with the pre= argument before checking it.

Check the Schema of a Table

The schema of a table can be flexibly defined with Schema and verified with col_schema_match().

Using Parquet Data

A Parquet dataset can be used for data validation, thanks to Ibis.