# Demos

Advanced Validation A validation with a comprehensive set of rules.

Starter Validation A validation with the basics.

Data Extracts Pulling out data extracts that highlight rows with validation failures.

Step Reports for Column Data Checks A step report for column checks shows what went wrong.

Step Report for a Schema Check When a schema doesn't match, a step report gives you the details.

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

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

Final Actions Execute actions after validation completes, such as sending alerts or generating summary reports. ------------------------------------------------------------------------ Set Failure Threshold Levels Set threshold levels to better gauge adverse data quality. 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). 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.