While testing, a wide spectrum of challenges and obstacles might arise which can impact the entire testing process and outcomes. When it comes to big data testing, the challenges get much more serious. Big Data testing is not a cake walk. Many complex hurdles might come in your path when you are conducting these complicated tests.
Just imagine you need to test 500 TB of unindexed and unstructured data which has does not have any attached cache. Is it scary to think right? In case you think that the challenges that you might face during the testing include slow process, unknown errors, faults during transmission and uncleaned data, you are nowhere close to the actual challenges that might come into the picture during the big data testing process.
Top Big Data Testing Challenges
The main challenges that you are likely to encounter while carrying out the big data testing have been captured here. This knowledge will help you to design a robust testing process so that the quality will not get compromised and you will be well-equipped to overcome these challenges.
1. The Huge Volume of Data
The huge volume of data that exists in Big Date is an extremely serious matter. Testing bulky and voluminous data is a challenge in itself. But this is just the tip of the iceberg. In the 21st century, most of the business undertakings need to store Petabyte or even Exabyte data that they have extracted from various offline and online sources.
The testers have to make sure that the data that is being tested and examined is of value for the business entity. The key issues that arise due to the high volume of data include storing the data carefully and preparing the test cases for the specific data that are not consistent. In addition to these hurdles, the full-volume testing of the massive data is next to impossible due to the size factor.
Also Read: Big Data and Analytics Testing: Learning the Basics
2. High Degree of Technical Expertise Requirements
If you think you will require an excel spreadsheet to test Big Data, you are living in a fairytale. As a Big Data tester, you need to have a proper understating of the Big Data ecosystem. You need to think beyond the usual parameters that are related to manual testing process and automated testing procedure.
If you are thinking to go with the automation testing process to make sure that no data is lost or promised, you are smart. This is because this machine-driven process makes ensure that all the range of probabilities is covered. But if you use this process, you will have to manage a large number of software which will make the testing process more complicated and complex.
3. High Costing and Stretched Deadlines
In case the Big Data testing procedure has not been properly standardized and strengthened for optimization and the re-utilization of the test case sets, there is a possibility that the test cycle will exceed the intended time-frame. This challenge could take a serious turn as the overall costs relating to testing will increase, and maintenance issues could arise as well.
The stretching of test cycles is not very uncommon when a tester has to handle and carefully check massive sets of data. As a Big Data tester, you need to make sure that the test cycles are accelerated. This is possible by focusing on robust infrastructure and using proper validation tools and techniques.
4. Future Developments
Big Data testing is quite different from the usual software evaluation process that one conducts from time to time. The Big Data testing is carried out so that new ways can be found to make some kind of meaning of the huge data volumes. The processes that are involved in testing big data must be carefully selected so that the ultimate data must make sense for the tester and the organization. The future development challenges arise in the case of big data testing as it focuses on the functionality aspect.
Also Read: Big Data Testing To Eliminate Data Complexities
5. Understanding the Data
To introduce and implement an effective big data testing model, it is necessary for the tester to have proper knowledge relating to volume, variety, velocity, and a value relating to the data. Such an understanding is of vital importance which acts as the real challenge for the tester who is carrying out the test process.
Understanding the data that has been captured in a huge quantity is something that is not easy. The tester might find it daunting to measure the testing efforts and strategic elements without having a proper idea about the data that has been captured. Similarly, it is paramount importance for a Big Data tester to get an idea about the business rules and the association that exists between the varying subsets of the data.
These are some of the common challenges that one might face while carrying out Big Data testing. It is necessary for a tester to carefully carry out the testing process so that the quality of the big data and the test results will not be compromised in any manner.
