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Data Validation Testing Tools and Techniques | Complete Guide

Chandan Gaur | 29 September 2022

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What is Data Validation Testing?

Data Validation testing is a process that allows the user to check that the provided data, they deal with, is valid or complete. Its testing responsible for validating data and databases successfully through any needed 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 testing, in which individual checks that the entered data valid or not according to the provided business conditions.
A type of testing in which individual units or functions of software testing. Its primary purpose is to test each unit or function.” Click to explore about, Unit Testing Techniques and Best Practices

How does it Work?

The data Validation process consists of four significant steps.

Detail Plan

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

Validate the Database

This is responsible for ensuring that all the applicable data is present from source to sink. This step is responsible for determining the number of records, size of data, comparison of source and target based on the data field.

Validate Data Formatting

The main focus is that the data clearly understood in the target system, the end-users should clearly understand data whether it is meeting the business expectation or not.

Sampling

Before testing on the large set of data, it is necessary to do sampling. It is essential to do testing on the 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.


Testing is defined as the variety of methods, tools, and practices used to justify that a software application works at many different levels or not. Source- Test Automation Framework

What are the benefits of it?

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 to Data Validation testing -
  1. Business requirement - It helps an individual to ensure that the data collected from different sources, maybe structured or unstructured, meet the Business requirement or not.
  2. Data Accuracy - In the future, the volume of data increases, and most probably, most of the data will 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 it's better to deal with the right kind of data only which meets business requirements.
  3. Better Decision Making
  4. Better Strategy and Enhanced Market Goals
  5. Increased Profits and Reduced Loss - If the data is accurate and correctly analyzed, then obviously there will be less loss, and on the other hand, there will be an increase in profit.

Why it is important?

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

How to adopt it?

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 or not.
  3. To verify that the provided data go successfully through transformations or not is by Data Transformation Testing.
  4. Data Quality testing to handle bad data.
  5. Database comparison testing to compare the source DB and target DB.
  6. End to End testing.
  7. Data warehouse testing.

Many organizations are moving into modern DevOps practices, also investing in building new projects into Microservice-based architecture. Source- Contract Testing for Applications

What are the best practises?

  • It is highly recommended to analyze the data to understand the requirement which is a need for business purposes.
  • Handle bad data correctly.
  • Use of the particular tool which fits perfectly between source and target.
  • Firstly, test on sample data instead of full complete data. This process will save time as well as resources also.
  • Compare the output result with the expected.

What are the best tools?

Various Data Validation Testing tools are available in the market for data validation. Some of them given below -
  1. Datameer
  2. Talend
  3. Informatica
  4. QuerySurge
  5. ICEDQ
  6. Datagaps ETL Validator
  7. DbFit
  8. Data-Centric Testing

Summarizing

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 key if we want to have reliable data that is consistent, accurate, and complete. In order to achieve efficient validation tests, 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 different benefits in this regard.