April 24, 2020
Big Data refers to structured, semi-structured and unstructured data of different types that is generated in a large volume and at a high velocity by applications. The size of Big Data begins from a few tera bytes to several petabytes. Traditional RDBMS, Relational Database Management Systems, such as Oracle, SQL, MySQL and Microsoft SQL server are not equipped to process Big Data since they it cannot be organized into rows and columns like structured data.
A variety of applications in the Telecommunications, social media, healthcare and finance sectors primarily rely on Big Data. Big Data helps organizations:
a) provide real time insights into the workflow of the organization,
b) gain actionable insights through enhanced business intelligence,
c) power Artificial Intelligence and Machine Learning for a variety of use cases,
d) understand customer insights,
e) mitigate potential risks and detect fraud, etc.
When working on a Big Data project, it is important to formulate a clear Quality Assurance strategy. It is important to carry out thorough functional and non-functional testing. In addition, it is imperative to validate the test data and manage the test environment.
The data is received from a variety of sources and devices which makes it crucial to have a quality data that allows the scope for accurate analysis.
The most important aspects of Big Data Testing include:
Functional testing for Big Data applications includes validation of the MapReduce process, validation of structured and unstructured data while validating data storage to ensure data is correct and of good quality.
a) Data, Process and Output Validation
Data flows into the big data system from a variety of sources such as biometric sensors, IoT devices, CSV files, information logs, RDBMS etc. The big data application relies on these data sets to function once this data is set up in Hadoop or a similar framework.
Prior to testing, this data must be cleaned and validated. After it is set up in Hadoop, it must be verified for accuracy. Once the logic is configured, the big data application will work accordingly. Data processing must also be validated for accuracy based on customer requirements.
After this, data subset is selected for testing purposes and the same process as customer requirements is applied. The result from the data subset and the results from data processing in the application are compared to validate the functioning of the application.
This data is then stored in the data warehouse from where it can be validated time and again to ensure it aligns with the larger data processing of the application. After the warehouse data is analysed, it is represented visually for Business Intelligence. After visual representation, it must be validated again.
b) Validation of MapReduce Process
The Hadoop framework allows for distributed processing of large data sets across computer clusters using Map/Reduce computation to divide the application into smaller fragments on multiple nodes in the cluster.
MapReduce process as the name suggests involves computation using two separate tasks – Map Task and Reduce Task. The map tasks produce sequence of key-value pairs depending on the code written for Map tasks which are in turn collected by the master controller, sorted by key and distributed among reduce tasks. Corresponding map and reduce key values end up together. Reduce tasks combine values combine all the values corresponding to the key operating at a time. This way all corresponding values end up together and are tracked by the Master Process.
It is important to validate this process to ensure the framework functions as intended.
Performance Testing and Failover Testing are two important pieces of the puzzle that validates the performance of the Big Data application. Performance testing is mainly carried out to get performance information analysis and accordingly eliminate bottlenecks.
a) Performance Testing
Performance Testing is essential to ascertain the performance and behavior of the big data application. It includes:
b) Failover Testing
Failover is a non-functional testing technique which deals with how system can allocate resources when a component fails. This validates a system's ability to move operations to back-up systems when the server crashes. It ensures the system can handle extra resources like additional computers or servers during critical situations.
Big Data applications working on Hadoop architecture, for instance, have hundreds of data nodes on the server. When the node fails, the HDFS components fail too. However, the HDFS architecture can automatically detect such failures and recover to continue the processing.
Failover testing, which captures metrics like Recovery Time Objective and Recovery Point Objective, is crucial to ensure that the recovery process and the data processing goes as planned even when the data nodes are changed.
These are the emerging trends in Big Data Testing that are being practiced in 2020. In addition to these, the other set of requisite tests including API testing, Unit Testing, Integration Testing and Regression testing must also be performed on the application as industry best practices dictate.
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