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. 我们必须记住，这些大数据的应用程序无法与传统的应用程序开发相比，. 所以测试过程也很复杂和挑战性的.
以下是一些重要的点，定义为大数据应用的测试计划和程序之前进行检查.
- 数据及其格式的源代码
- 数据量和数据生成的速度
- 测试数据准备 (样品和实际)
- 个别组件测试
- 完成应用测试
- 可靠性, 应用程序的稳定性和性能
现在, 让我们来看看新兴的趋势，大数据应用测试领域.
实时数据集成测试: 在今天的大数据应用, 需求是饲料的实时数据，并获得实时分析. 有多个信息源, 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.
