Today, companies all over find themselves inundated with data. This big complex data gives these companies a hard time. They find it difficult to process, manage, and analyze it for their progress. For extracting the maximum value out of it, they require a dynamic Big Data testing mechanism in place.
Data is being generated at a rapid pace. In the near future, it will only expand further with the number of connected devices crossing 41.6 billion by 2025. Before moving onto the various Big Data testing methods, it is essential to get clarity on what actually Big Data entails.
Data is being generated at a rapid pace. In the near future, it will only expand further with the number of connected devices crossing 41.6 billion by 2025. Before moving onto the various Big Data testing methods, it is essential to get clarity on what actually Big Data entails.
According to Gartner, the high-volume, high variety, or velocity assets of information are termed as Big Data. It demands advanced and innovative processing mechanisms that enable organizations to derive valuable insights and, as a consequence, improve its products and services.
Big companies like Facebook and Twitter generate up to 4 Petabytes and 12 Terabytes of data per day. It is generated as structured, unstructured, and semi-structured data.
Examples of structured data include databases, data warehouses, and enterprise systems like CRM, ERP, etc. Unstructured ones include images, videos, mp3 files, among many. Semi-structured data are those not rigidly organized and contain various tags like XML, CSV, and JSON.
Big Data testing primarily refers to the process of validating the major functionalities of Big Data applications. Nowadays, businesses are eager to avail of the Big Data testing and QA testing services of a software testing company. Nevertheless, the immense complexity of Big Data makes its testing dramatically different from normal software testing.
Big Data testing - What is it?
The defining features of Big Data are Volume, that is, the size of the data. Velocity, that is, the speed at which data is produced.Variety, that is, the different kinds of data produced.Veracity, that is, the data’s trustworthiness. Value, that is, how Big Data can be transformed into valuable business insight.
Given below are Big Data testing techniques used to test a particular functionality of Big Data.
Methods of Big Data Testing
There are several different techniques used for testing Big Data. These testing strategies cannot be accomplished without the following prerequisites:
- Highly skilled and qualified software testing company experts.
- Powerful automation testing tools.
- Readily available processes and mechanisms that will work to validate the movement of data.
Given below are Big Data testing techniques used to test a particular functionality of Big Data.
- Data Analytics and Visualization testing test its volume.
- Its velocity is measured through migration and source extraction testing.
- Its variety is validated by performance and security testing.
- Its veracity is validated by Big Data ecosystem testing.
Major components of Big Data testing strategies.
- Data staging process
- MapReduce validation
- Output validation
- Data staging process
- MapReduce validation
- Output validation
System architecture testing
Architecture testing is indispensable to a successful Big Data project. Hadoop processes huge volumes of data. Its poor architecture may lower its performance; consequently, it will not be able to accomplish the requirements. Hence, Performance and Failover test services like testing job completion time, data throughput, memory utilization, etc. should be done in the environment of Hadoop.
Performance testing
Performance testing involves the following:
- Data ingestion:
- Processing of the data:
- Testing the performance of individual components:
Big Data testing Environment Needs
The test environment differs according to the application being tested. Big Data testing demands a test environment that comprises the following:
- Adequate storage space, along with the ability to process huge volumes of data.
- It should be resource-intensive with minimal CPU and memory consumption to keep its performance high.
- Clusters having distributed nodes and data is another requirement for the testing environment.
Hence, we see that the characteristics of Big Data demand a testing process that is radically different from conventional software testing. It, therefore, requires highly skilled QA testing services experts to effectively carry out the testing of its each and every functionality.
Automation testing tools for Big Data
Big Data testing is conducted using multiple automation testing tools, all of which integrate well with Hadoop, MongoDB, AWS, etc. All of the tools need to have certain features like scalability, dependability, economic feasibility, and a robust reporting functionality. Some of the commonly used ones include the Hadoop Distributed File System (HDFS), MapReduce, HiveQL, HBase, and Pig Latin.
Conclusion
The importance of Big Data remains undeniable for companies worldwide. The key benefits of a successful Big Data processing and analysis include optimized decision-making and enhanced financial performance. It plays a big role in serving customers better and forging a long term relationship with them. With more and more businesses depending on Big Data analysis, we can only hope to see more of its robust testing techniques being developed in the future.