Deequ

Load customer data into a Spark DataFrame.

Define checks for completeness of required fields (e.g., name, email, address).

Define checks for uniqueness of customer IDs.

Define checks for data type validation (e.g., email format, phone number format).

Run the VerificationSuite to identify any violations.

Quarantine and fix the data to meet data quality requirements before loading into the CRM.

Load log data into a Spark DataFrame.

Define checks for expected log message formats.

Define checks for the presence of required fields (e.g., timestamp, log level, message).

Define checks for detecting unusual log patterns or error rates.

Run the VerificationSuite to identify any anomalies.

Trigger alerts or automated remediation based on the detected issues.

Load financial transaction data into a Spark DataFrame.

Define checks for data accuracy (e.g., ensuring amount values are within expected ranges).

Define checks for completeness of required fields (e.g., transaction ID, account number, date).

Define checks for consistency between related data elements.

Run the VerificationSuite to identify any data errors.

Investigate and correct any detected errors before generating financial reports.

Load training data into a Spark DataFrame.

Define checks for data completeness, accuracy, and consistency.

Define checks for feature distributions and statistical properties.

Run the VerificationSuite before training the model.

Quarantine and fix any data quality issues.

Retrain the model with validated data.

Load data from the source and target systems into separate Spark DataFrames.

Define checks for data completeness (e.g., record counts).

Define checks for data consistency between corresponding fields in the source and target systems.

Run the VerificationSuite to identify any discrepancies.

Investigate and reconcile any data migration issues.