Big Data Testing Services

Big Data Testing Services We Offer

Functional Testing

We check the entire front end of the app, from the data visualization procedure to its reception in accordance with user requirements, to compare the actual results with the expected ones and get a complete picture of the app structure.

Non-Functional Testing

This kind of big data analytics testing services includes validating the volume, speed, and variety of big data to ensure that the infrastructure is functioning correctly, the security of sensitive data is guaranteed, and resilient mechanisms are in place.

Performance Testing

This testing focuses on verifying the performance of all components of a big data system in a variety of environments with different types and volumes of data to provide efficient storage, processing, and retrieval of large datasets.

Big Data Testing Platforms

Tools We Use for Big Data Testing

Solutions For Which We Implement Big Data Testing

Databases and Data Warehouses

Enterprise Systems (CRM, ERP, etc.)

Analog Data

Our Approach to Big Data Testing

Implementing live integration

Data ingestion testing

Data processing testing

Data storage testing

Data quality testing

Data migration testing

F.A.Q

What is big data testing?

Big data testing is a functional and non-functional verification of the correct operation of big data software and applications. Big data is a huge amount of data that cannot be processed by traditional systems.

How do you test big data projects?

Big data projects can be tested by functional and non-functional methods. The key types of testing for big data are functionality, performance, and security testing.

What is the data that can be tested for big data testing?

The data that can be tested as part of big data testing is divided into 3 types:

Structured data. This is data that can be organized into tables, stored, and processed within its format.
Semi-structured data. This is metadata that has a certain form, but is not available for structuring into tables, and requires special operations.
Unstructured data. This is data without a structure that is difficult to extract, process, and store.

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