What are the 5 Trends for Testing in the Era of Big Data?
In today’s world of data explosion, big data applications and their implementations are growing dramatically. As data is at the heart of any big data application, it is important to understand the characteristics of big data. The three most unique characteristics of big data are ‘Volume’, ‘Velocity’ and ‘Variety’. And these data comes in different format from multiple channels. All of these factors rule the development and testing process of big data applications. Proper understanding of big data characteristics is critical for a successful testing.
All big data applications are different in terms of their nature and complexity. And we must remember that these big data applications cannot be compared with traditional application development. So the testing process is also very complex and challenging.
Following are some of the important points to be checked before defining the testing plan and procedure for big data applications.
- Source of data and its format
- Data volume and speed of data generation
- Test data preparation (sample and actual)
- Individual component testing
- Complete application testing
- Reliability, stability and performance of the application
Now, let us have a look at the emerging trends in the field of big data application testing.
Live data integration testing: In today’s big data application, the demand is to feed live data and get real time analysis. There are multiple sources of information, so the live integration is a complex task. The analysis is based on the live input data, so the companies should ensure clean and reliable data. The reliability and quality of data should be tested properly from source to destination.
Instant application deployment testing: Most of the big data applications are developed for predictive analytics. These analytics applications are dependent on instant data collection and deployment. The analytics insights from this larger volume of data (known as big data) are very important for business decisions. And the instant deployment is very critical for the success of ever changing business dynamics. So the testing of the application and the data is essential before live deployment.
Scalability testing: The data volume in any big data application is huge, so the trend in scalability testing is increasing day by day. The amount of data and its processing is a complex task. To support the increasing load, the application architecture should be tested properly with smart data samples. Scalability testing in a big data applications is a challenging task. The application should be able to scale up without compromising the performance.
Security testing: Security testing is another emerging trend in all big data applications. Big data applications mainly work on various types of data from different sources. So the security of this huge volume of data should be ensured while developing the application. The confidential data, which is processed on a big data platform, should not be exposed in public. To ensure data security and privacy, different mechanisms are applied in different layers. Proper testing is very important to prevent various security threats.
Performance testing: Performance of big data applications are critical, because they work on live data and provide analytics insight. So the performance testing of a big data application is essential with scalability support.
We have discussed different dimensions and trends in big data application testing. Hope this will help you to get an overview of big data testing.
|Reference:||What are the 5 Trends for Testing in the Era of Big Data? from our JCG partner Kaushik Pal at the TechAlpine – The Technology world blog.|