Data Validation Testing
What Is Data Validation?
The process of checking the quality and accuracy of the data source is called data validation. Data validation is done before the data is used, imported, and processed. There are various types of data validations such as data migration testing, data integrity testing and there is another concept of training data as-well. The type is decided based on the requirements, destination constraints, and/or data collection objectives. It is safe to say that data validation is a foundation of data cleansing.
Today, all clients demand leads to big data that gives them a competitive edge based on its accuracy and correct interpretation. Every second the size of this data increases thus it gets harder and harder to manage. Data needs to be used for business reporting otherwise it is of no use. To deal with the complexities of ever-growing data, new techniques, business rules, and intelligence needs to be added to the current systems. This process is demanding and tedious. Thus data validation is needed to make sure there aren’t any errors along the way.
How Data Validation Became Important?
When data from various sources is merged, then all repositories need to be compatible and follow the same business rules without corrupting any data field. Inconsistencies are common when data validation is not performed. Inconsistencies can arise in the type of data or context of data. With data validation testing, the main target is to make combined data accurate, consistent, complete, and free of any data losses. This was constantly happening in enterprises this the need to start data validation came into being.
What Is Data Validation Testing?
The process of performing data validation as part of testing is called data validation testing. Data validation testing allows the end-user to see if the available data is valid or not. This testing is performed on databases after transformations are applied to it. This testing also verifies that the databases are following business rules and are compatible. It is sort of a database testing.
Data validation testing makes sure that the data integrity will not be affected when extraction, transformation, and loading is performed. It also states what to do with incorrect and inconsistent data in the form of test cases.
Data Validation Testing for Enterprises and ETL Projects
It is important to test the data of big enterprises as they are dealing with big data. When there is big data involved, it is important in the data collection phase. So data validation testing ensures that data is not corrupted. This also ensures information authenticity and validation. It is also important to do this where ETL is involved.
ETL projects involve extraction of data, application of logical rules, and transformed then is loaded in the target location. Such steps require data validation to ensure correctness and to make sure error is not propagated in the data pipeline. Another important reason is to keep an eye on data losses and discrepancies.
Data Validation Testing for Migration Projects (Data Migration Testing)
When projects involve data migrations, data validation testing becomes important again. In such projects, data in huge volumes is moved from source to target storage. This is carried out due to many reasons such as upgradation purposes, new technology compatibility purposes, and optimization purposes.
New target locations are mostly highly usable, disruption-free, and smooth. Such steps require data validation to ensure that migration has not affected the system badly in any way.
In such scenarios of data migration testing, some common data validation tests are checking the number of rows migrated, functionality tests such as providing the same input to both systems and comparing results. Other common tests involve performance, security, E2E, and regression tests.
Steps of Data Validation Testing
Data validation testing has four main stages; detailed plan, database validation, data formatting validation, and sampling. Let’s have a look at each one.
- Detailed Plan: consists of creating a blueprint and a road map to carry out the validation process. The plan aims to find problems in the source and its resolution plan is defines. Detailed planning makes the testers decide the iterations that would be needed to validate the data.
- Database Validation: consists of steps to ensure that data is available from source till destination. Source and target data fields are compared in terms of the number of rows, data size, and overall schema comparisons.
- Data Formating Validation: consists of focusing on the data of the target, to make sure data is understood by the user and all business expectations are met.
- Sampling: consists of testing small sets of data before processing is consumed on testing larger data sets. This decreases the wastage of processing power if blunders are made by being identified in the smaller data sets.
Benefits of Data Validation Testing
To realize the importance of data validation and migration testing, it is important to see the benefits of performing tests on integration, and migration by performing validation and verification of data.
All these testing efforts result in improved data collection and healthy data which can provide reliable quantitative results.
Let’s see the benefits of data validation and several testing approaches.
- Improved fulfillment of business requirements.
- Improved data accuracy.
- Improved decision making.
- Improved business strategic management.
- Improved profits.
What is Database (DB) Validation Testing?
Other than data validation, database validation is also important. Database validation testing involves stored data and metadata validation. The testing is done based on requirements against the quality of data and performance of the data. This also looks into the objects, functionality, data types, and lengths before it is made live and available for users. Indexes are also checked and the entire environment where data will be moving and evolving is checked against set parameters.
Some common types of database validation testing are:
- Data Mapping.
- ACID Validation.
- Data Integrity Checks.
- Business Rule Compliance Test.
- Data Accuracy Test.
- Data Completeness Test.
- Data Transformation Test.
- Data Quality Test.
- DB Comparison Test. (comparison between source and target)
- End-to-End Tests.
- Data Warehouse Test.
Data errors can be prevented with proactive and continuous testing of all such sorts.
Steps to Adopt Data Validation Testing
Tests described above can help to easily adopt data validation testing. Let’s look deeper.
Data accuracy and data completeness tests ensure that data is correct. Data transformation tests verifies the data is not corrupted after transformation. Then data quality test handles the bad data. Then database comparison test compares the source and target database and end-to-end and data warehouse tests also help achieve data validation tests.
Realizing that all these tests are much intensive, we can conclude that data validation takes a lot of effort. Data quality is important for the organization nowadays for correct business intelligence to achieve greater return on investment.
Data Validation can be done on Excel as-well using Pivot Tables and Power Query.
BiG EVAL provides a solution for data validation testing called the BiG EVAL DTA tool. It automates test processes in data-oriented projects, data migration, data imports and exports, integration of interfaces, and many more.
All in all, BiG EVAL automates your testing processes within all phases of your continuous delivery process and ensures - using quality gates - that only verified system components can be deployed.
BiG EVAL supports a variety of data sources and technologies within your test cases and quality checks such as SQL server, Oracle, MySQL, Postgre SQL, and Azure SQL Database.