Reporting Validation

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Currently, we only support "dataset mapping" functionality. Other functions of reporting validation including fix model and field's error will be supported in the future.

Introduction

Reporting validation is the process of searching and validating your reporting items (dashboard, widgets, datasets) that are currently referring to the AML models and Datasets in Data Modeling.

This is necessary for you to validate your AML reference thus fixing errors or updating the name of your AML reference across your project

Use cases and benefits

Fix errors after a deployment

You can use Reporting validation to find and fix errors caused by changes in your modeling layer.

For example, you change the model name from turnover to revenue, all the reports, filters in Reporting that included fields of turnover model will be no longer functional.

Reporting validation will help you to find these errors and fix them before or right after deploying.

Find and replace names of fields, models, or datasets

You can also use Reporting Validation to search and replace names for models, datasets, and fields.

How to use Reporting Validation

Dataset mapping

If any widget (report and filter) in reporting references deleted/renamed datasets, the error will be raised and you will need to fix the error before continuing with the deployment.

Changes that might cause this error:

• Alter the dataset name in Data Modeling
• Remove one of the existing datasets in the index.aml file

For example, I change my dataset name from ecommerce to ecommerce_khai in my dataset file and index.aml file.

After clicking Deploy to production, the Error will be raised because there are reports created from that dataset in Reporting.

You will need to either Fix the error or Cancel the Deployment to continue.

By clicking on Update, you will be able to update your widgets and point them to another dataset listed in the file index.aml.

Run Reporting Validation

Currently, this option is not supported yet but in the future, we will allow you to fix errors at the field and model level.