Big Data Testing Services We Offer

Functional Testing

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

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

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

What is big data testing?

Big data testing is the process of testing a big data application to ensure that all of its features are working as expected. The goal of big data testing is to ensure that the system runs smoothly and without errors while maintaining security and performance.

Traditional computing technologies cannot handle big data sets. Testing these sets requires specific tools, methodologies, and platforms. Testing big data apps is much more difficult than testing regular apps. Big data automation testing tools help streamline and speed up repetitive tasks.

The definition of big data makes it clear that we are talking about one of three options:

  • amount of data over 10 TB;
  • tables so large that they cannot be opened and analyzed in Excel;
  • unstructured data from different sources of different sizes.

There is also the so-called 3V-theorem, which reveals the essence of the term big data from the other side:

  1. Volume – data volume. It's really big. Usually it is more than 50 TB and ad infinitum.
  2. Velocity – data is regularly updated, so it requires constant processing (from batch processing, when data is loaded in batches, for example, once a day, to real time streaming, when real-time data gets into charts/reports and even affects decision-making systems on based on ML).
  3. Variety – various data can have completely different formats, be unstructured or partially structured (from CSV/Avro files with a clear structure to logs in a stream).

There is no pure back-end (API) or front-end parts here. That is, we have:

  • input data that we practically cannot control;
  • data processing (the software part that is responsible for ETL – extract, transform, load);
  • tables/files /visualization in the output.

Obviously, in this case, an end-to-end test is a check whether, for example, a line from a CSV file at the input passed all transformations and is displayed correctly in the report. Ideally, of course, we want to check if all input data is displayed correctly in the final reports.

How do you test big data projects?

There are two main types of testing: non-functional and functional. Non-functional testing includes performance testing, security testing, stress testing, etc. For big data QA, the focus is on performance, functionality and security. Functional testing is divided into four types.

  1. Metadata testing. We check the metadata of the actual data (length, type) and tables (date of modification, date of creation, number of rows, indexes, etc.).
  2. Data validation testing. We check if the data went through all the transformations correctly. Unix timestamp to date conversion can be used as an example.
  3. Completeness testing. We check if all source data is processed correctly. (The data that was successfully parsed ended up in the staging layer, and if not, then in the error tables or simply entered the logs.)
  4. Accuracy testing. We check the correctness of the table transformation logic along the path from staging to the analytics layer. (This is usually done by creating appropriate validation views using SQL.)

Big data performance testing plays a key role because it affects the speed of processing terabytes of data. To implement such testing, special skills and experience are needed to implement the following scenarios:

  1. Batch data processing. Analysis of a packet of data that has already been saved over a period of time as it passes through the system.
  2. Real-time data processing. Analysis of data that are processed in the system in real time and checking their stability.
  3. Interactive data processing. Analysis of data when interacting with the system from the point of view of a real user.

The general approach to big data software testing looks like this:

  1. Data validation. Loading data from a source into a big data system using special tools to extract and check it for errors and missing values.
  2. Data processing. Create key-value pairs for the data and validate the data to make sure the MapReduce logic works as expected.
  3. Output validation. Checking data integrity, transformation logic, location accuracy of key-value pairs, and transferring data to the data warehouse.
What is the data that can be tested for big data testing?

There are data formats that can handle big data:

  1. Structured data. Any tabular data that is conveniently organized in rows and columns. They can also be stored, accessed and processed in a fixed format form. Over the years, computer science has made great strides in improving techniques for working with this type of data (where the format is known in advance) and learned how to benefit. However, already today there are problems associated with the growth of volumes to sizes measured in the range of several zettabytes.
  2. Semi-structured data. Metadata, tags, or duplicate values that require certain operations to be loaded. Semi-structured data has some form but is not really defined using tables in relational databases. An example of this category is personal data presented in an XML file.
  3. Unstructured data. Data that does not obey any structure and is difficult to retrieve and store. A typical example of unstructured data is a heterogeneous source containing a combination of simple text files, pictures, and videos. Organizations today have access to large amounts of raw or unstructured data, but do not know how to benefit from it.
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