DevOps
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Big Data Testing is a testing process of a big data application in order to ensure that all the functionalities of a big data application works as expected. The goal of big data testing is to make sure that the big data system runs smoothly and error-free while maintaining the performance and security.
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques, and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity. You can learn more about Big Data, Hadoop and MapReduce here
In this Big Data Testing tutorial, you will learn-
Testing Big Data application is more verification of its data processing rather than testing the individual features of the software product. When it comes to Big data testing, performance and functional testing are the keys.
In Big Data testing strategy, QA engineers verify the successful processing of terabytes of data using commodity cluster and other supportive components. It demands a high level of testing skills as the processing is very fast. Processing may be of three types
Along with this, data quality is also an important factor in Hadoop testing. Before testing the application, it is necessary to check the quality of data and should be considered as a part of database testing. It involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, data completeness, etc. Next in this Hadoop Testing tutorial, we will learn how to test Hadoop applications.
The following figure gives a high-level overview of phases in Testing Big Data Applications
Big Data Testing or Hadoop Testing can be broadly divided into three steps
The first step in this big data testing tutorial is referred as pre-Hadoop stage involves process validation.
Tools like Talend, Datameer, can be used for data staging validation
The second step is a validation of "MapReduce". In this stage, the Big Data tester verifies the business logic validation on every node and then validating them after running against multiple nodes, ensuring that the
The final or third stage of Hadoop testing is the output validation process. The output data files are generated and ready to be moved to an EDW (Enterprise Data Warehouse) or any other system based on the requirement.
Activities in the third stage include
Hadoop processes very large volumes of data and is highly resource intensive. Hence, architectural testing is crucial to ensure the success of your Big Data project. A poorly or improper designed system may lead to performance degradation, and the system could fail to meet the requirement. At least, Performance and Failover test services should be done in a Hadoop environment.
Performance testing includes testing of job completion time, memory utilization, data throughput, and similar system metrics. While the motive of Failover test service is to verify that data processing occurs seamlessly in case of failure of data nodes
Performance Testing for Big Data includes two main action
Performance testing for big data application involves testing of huge volumes of structured and unstructured data, and it requires a specific testing approach to test such massive data.
Performance Testing is executed in this order
Various parameters to be verified for performance testing are
Test Environment needs to depend on the type of application you are testing. For Big data software testing, the test environment should encompass
Properties | Traditional database testing | Big data testing |
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Data |
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Testing Approach |
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Testing Strategy |
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Infrastructure |
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Validation Tools | Tester uses either the Excel-based macros or UI based automation tools | No defined tools, the range is vast from programming tools like MapReduce to HIVEQL |
Testing Tools | Testing Tools can be used with basic operating knowledge and less training. | It requires a specific set of skills and training to operate a testing tool. Also, the tools are in their nascent stage and over time it may come up with new features. |
Big Data Cluster | Big Data Tools |
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NoSQL: |
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MapReduce: |
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Storage: |
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Servers: |
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Processing |
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Automation testing for Big data requires someone with technical expertise. Also, automated tools are not equipped to handle unexpected problems that arise during testing
It is one of the integral phases of testing. Virtual machine latency creates timing problems in real time big data performance testing. Also managing images in Big data is a hassle.
Performance testing challenges
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