Organizing Projects

How Many Projects Do I Need?

Projects house testing and data validation work. In simple terms, a project is where you'll create tests, build jobs, execute those tests, and finally, view the results.

The first thing to consider is how you want to group testing and validation work. Typically Validatar Customers use:

• A single project when the team requires uniform visibility into tests, results, and data sources. This setup fosters enhanced transparency and consistency in comprehensive data testing. Moreover, it is advantageous when all tests pertain to the same business project or data source, streamlining organization and management.
• Multiple Projects if you seek finer control over content visibility or wish to maintain separation of testing use cases. Creating two or more projects allows for distinct management of testing scenarios. Furthermore, if you have multiple business projects or teams with minimal testing overlap, establishing a separate project for each group facilitates streamlined organization and tailored collaboration.

Example Project Use Cases

• ETL Testing for Data Warehouses
  • Ensuring the accuracy and integrity of data during extraction, transformation, and loading processes.
  • Validating the consistency and completeness of data across different data sources and target systems.
• Data Quality Testing
  • Verifying the quality, correctness, and reliability of data.
  • Detecting and resolving data anomalies, errors, and inconsistencies.
• Data/Cloud Migration Testing
  • Verifying the successful transfer of data from on-premise systems or legacy infrastructure to cloud-based environments.
  • Testing the interoperability of data formats, schemas, and protocols between the on-premise system and the cloud environment.
• Regression Testing
  • Verifying the stability and integrity of data after system updates, data changes, or enhancements.
  • Identifying and resolving any unintended side effects or regressions that may impact data accuracy or functionality.
• Metadata Validation
  • 
    

Validatar supports numerous use cases for each project, but the key focus revolves around data validation by comparison. By having an expected result and a baseline data set, you can effortlessly create Validatar tests, enabling comprehensive data testing and validation.

Other Ways to Organize Projects

Here are some other ways Validatar users organize their projects:

• By Environment - Ideally, if you have multiple development environments (DEV, QA, PROD, etc.), you can keep relevant tests for each environment in separate projects. Validatar makes it easy to move tests from one project to another, allowing seamless transitions between projects specifically designed for different environments. This flexibility ensures that you can maintain the same set of tests while effortlessly switching to the appropriate data source for each specific environment.
• By Project Team - If you have multiple teams using Validatar, you can create a project for each team. Usually, separate teams have separate functions, data sources, and processes they're responsible for. Having a project for each team ensures that the right teams have the proper access to your organization's data assets and increases collaboration amongst team members.
• By Task - Validatar can help businesses in a myriad of ways. Create a Validatar project for each internal project the organization has to ensure data quality is being monitored and is consistent throughout your business. 

Folder Organization Within Projects

You can organize tests and jobs in Projects by using Folders. Here are a few suggested ways to use folders for each potential project use case.

• ETL Testing for Data Warehouses
  • Extraction Validation
    • Record Counts
  • Transformation Testing
  • Loading Verification
  • Data Consistency Checks
• Data Quality Testing
  • Data Completeness
  • Data Uniqueness
  • Data Validation
  • Data Consistency
  • Data Accuracy
  • Data Timeliness
• Data/Cloud Migration Testing
  • Pre-Migration Checks
  • Migration Validation Checks
  • Data Synchronization Checks
  • Post-Migration DQ Assurance
• Metadata Validation
  • Data Relationships Verification
  • Metadata Consistency Checks
  • Data Mapping Validation
  • Data Type Validation