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. Die aansoek moet in staat wees om skaal sonder om die prestasie.
sekuriteit toetsing: Sekuriteit toetsing is 'n ander ontluikende tendens in alle groot data aansoeke. Big data aansoeke werk hoofsaaklik op verskillende tipes van data uit verskillende bronne. So het die veiligheid van hierdie groot volume van data moet verseker word, terwyl die ontwikkeling van die aansoek. Die vertroulike inligting, wat verwerk op 'n groot data platform, moet nie blootgestel word in die openbaar. Om die beveiliging van data en privaatheid te verseker, verskillende meganismes toegepas in verskillende lae. Behoorlike toets is baie belangrik om verskeie sekuriteit bedreigings te voorkom.
prestasietoetsing: Prestasie van die groot data aansoeke is van kritieke belang, omdat hulle werk op live data en bied Analytics insig. So het die prestasie toetsing van 'n groot data aansoek is noodsaaklik met scalability ondersteuning.
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.
