Data Validation Testing Tools and Techniques

Data Validation Testing Tools and Techniques | Complete Guide

6:12

What is Data Validation Testing?

Data Validation testing is a process that allows the user to check that the provided data is valid or complete. Data Accuracy Testing is responsible for validating data and databases successfully through any needed data transformations without loss. It also verifies that the database stays with specific and incorrect data properly.

In simple words, it is a part of Database and Data Quality Testing, in which the system validates and checks whether the entered data is valid or not according to the provided business conditions.

Here are some of the types of data validation Testing:

1. Data Type Check
2. Code Check
3. Range Check
Format Check

Unit testing is a software development process in which the smallest testable parts of an application, called units, are individually scrutinized for proper operation.

Top data validation testing tools

Various Data Validation Testing tools are available in the market for data validation. Some of them are given below -

Datameer
Talend
Informatica
QuerySurge
ICEDQ
Datagaps ETL Validator
DbFit
Data-Centric Testing

What is the data validation testing techniques?

The data Validation process consists of four significant steps.

1. Detail Plan

The most critical step is to create the proper roadmap for it. It deals with the overall expectation: if there is an issue in source data, how do we resolve it? It also deals with defining the number of iterations required during it.

2. Validate the Database

This ensures all applicable data is present from source to sink. This step determines the number of records, data size, and source and target comparison based on the data field.

Get more information regarding the Database.

3. Validate Data Formatting

The main focus is that the target system clearly understands the data; the end-users should know whether it meets the business expectations.

4. Sampling

Before testing on a large set of data, it is necessary to do sampling. It is essential to try a small amount of data and check if the sample data meets the business requirement; if yes, then only proceed with a large set of data. It will also decrease the error rate for data and increase the quality and accuracy of the data.

Test automation refers to the practice of using software tools and scripts to automate the execution of tests in software development and quality assurance processes.

What are the Benefits of Data Validation Testing?

Data Validation testing ensures that the data collected is accurate, qualitative, and healthy. Is the collected data from different resources meet the business requirement or not? Below are several benefits of Data Validation testing -

Business requirement - It helps an individual ensure that the data collected from different sources, whether structured or unstructured, meets the Business requirement.
Data Accuracy - In the future, the volume of data will increase, and most of the data will probably be unstructured. It's impossible to imagine analyzing this amount of data. Before mining, it is necessary to convert this data into a structured format. So, dealing with the correct data that meets business requirements is better.
Better Decision Making
Better Strategy and Enhanced Market Goals
Increased Profits and Reduced Loss - If the data is accurate and correctly analyzed, there will be less loss; on the other hand, profit will increase.

Why Data Validation Testing is essential?

Regarding Big Data, it is one of the most critical components of data collection. Testing matters because it helps an individual ensure that the data is not corrupted and is responsible for checking whether the provided data is accurate. It also helps verify whether the information provided is validated against the business requirement. The initial data is fed into the Hadoop Distributed File System (HDFS) and validated.

How do we adopt data validation testing?

There are various approaches and techniques to accomplish Data Validation testing.
1. Data Accuracy testing to ensure that the provided data is correct.
2. Data Completeness testing to check whether the data is complete.
3. To verify that the provided data goes successfully through transformations or not by Data Transformation Testing.

Research in-depth about data transformation.
4. Data Quality testing to handle insufficient data.
5. Database comparison testing to compare the source DB and target DB.

Know more about database testing.

6. End-to-end testing.
7. Data warehouse testing.

Contract testing is a methodology for ensuring that two separate systems (such as two microservices) are compatible and can communicate with one other.

What are the best practices for data validation testing?

1. It is highly recommended to analyze the data to understand the requirement, which is a need for business purposes.
2. Handle insufficient data correctly.
3. Use the particular tool that fits perfectly between source and target.
4. First, test on sample data instead of whole, complete data. This process will save time as well as resources.
5. Compare the output result with the expected.

Complete Guide to Data Validation Testing Techniques

In the current IT context, characterized by the multiplicity of sources, systems, and repositories, data movement processes are a challenge in projects that contain phases of migration, integration, or updating of information. For almost all of them, performing data validation is critical if we want to have reliable data that is consistent, accurate, and complete. To achieve efficient validation tests that are easy to execute and in line with current requirements, it is necessary to have solutions that optimize them through different options and automation, among others, the Informatica Data Validation Option (DVO), a complementary tool to PowerCenter that combines various benefits in this regard.