Hadoop Testing Interview Questions
Hadoop Testing Interview Questions With Answers
What is Hadoop Big Data Testing?
Big Data means a vast collection of structured and unstructured data, which is very expansive & is complicated to process by conventional database and software techniques. In many organizations, the volume of data is enormous, and it moves too fast in modern days and exceeds current processing capacity. Compilation of databases that are not being processed by conventional computing techniques, efficiently. Testing involves specialized tools, frameworks, and methods to handle these massive amounts of datasets. Examination of Big data is meant to the creation of data and its storage, retrieving of data and analysis them which is significant regarding its volume and variety of speed.
What is Architecture Testing?
This pattern of testing is to process a vast amount of data extremely resources intensive. That is why testing of the architectural is vital for the success of any Project on Big Data. A faulty planned system will lead to degradation of the performance, and the whole system might not meet the desired expectations of the organization. At least, failover and performance test services need proper performance in any Hadoop environment.
What is Performance Testing?
Performance testing consists of testing of the duration to complete the job, utilization of memory, the throughput of data, and parallel system metrics. Any failover test services aim to confirm that data is processed seamlessly in any case of data node failure. Performance Testing of Big Data primarily consists of two functions. First, is Data ingestion whereas the second is Data Processing
What are the general approaches in Performance Testing?
Method of testing the performance of the application constitutes of the validation of large amount of unstructured and structured data, which needs specific approaches in testing to validate such data.
Setting up of the Application
Designing & identifying the task.
Organizing the Individual Clients
Execution and Analysis of the workload
Optimizing the Installation setup
Tuning of Components and Deployment of the system
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What is the difference Big data Testing vs. Traditional database Testing regarding Infrastructure?
A conventional way of a testing database does not need specialized environments due to its limited size whereas in case of big data needs specific testing environment.
What are Functional testing of big data applications?
Functional testing of big data applications is performed by testing the front end application based on user requirements. The front end can be a web based application which interfaces with Hadoop (or a similar framework on the back end).
Results produced by the front end application will have to be compared with the expected results in order to validate the application.
Functional testing of the applications is quite similar in nature to testing of normal software applications.
What are the challenges in Large Dataset in the testing of Big data?
Challenges in testing are evident due to its scale. In testing of Big Data:
We need to substantiate more data, which has to be quicker.
Testing efforts require automation.
Testing facilities across all platforms require being defined.
What are other challenges in performance testing?
Big data is a combination of the varied technologies. Each of its sub-elements belongs to a different equipment and needs to be tested in isolation. Following are some of the different challenges faced while validating Big Data:
There are no technologies available, which can help a developer from start-to-finish. Examples are, NoSQL does not validate message queues.
Scripting: High level of scripting skills is required to design test cases.
Environment: Specialized test environment is needed due to its size of data.
Supervising Solution are limited that can scrutinize the entire testing environment
The solution needed for diagnosis: Customized way outs are needed to develop and wipe out the bottleneck to enhance the performance.
What are the different types of Automated Data Testing available for Testing Big Data?
Following are the various types of tools available for Big Data Testing:
Big Data Testing
ETL Testing & Data Warehouse
Testing of Data Migration
Enterprise Application Testing / Data Interface /
Database Upgrade Testing
What are Big Data Automation Testing Tools?
Testing big data applications is significantly more complex than testing regular applications. Big data automation testing tools help in automating the repetitive tasks involved in testing.
Any tool used for automation testing of big data applications must fulfill the following needs:
Allow automation of the complete software testing process
Since database testing is a large part of big data testing, it should support tracking the data as it gets transformed from the source data to the target data after being processed through the MapReduce algorithm and other ETL transformations.
Scalable but at the same time, it should be flexible enough to incorporate changes as the application complexity increases
Integrate with disparate systems and platforms like Hadoop, Teredata, MongoDB, AWS, other NoSQL products etc
Integrate with dev ops solutions to support continuous delivery
Good reporting features that help you identify bad data and defects in the system
What are advantages of using Big Data / Hadoop?
Scalable : Big data applications can be used to handles large volumes of data. This data can be in terms of petabytes or more. Hadoop can easily scale from one node to thousands of nodes based on the processing requirements and data.
Reliable : Big data systems are designed to be fault tolerant and automatically handle hardware failures. Hadoop automatically transfers tasks from machines that have failed to other machines.
Economical : Use of commodity hardware along with the fault tolerance provided by Hadoop, makes it a very economical option for handling problems involving large datasets.
Flexible : Big data applications can different types of heterogeneous data like structured data, semi structured data and unstructured data. It can process data extremely quickly due parallel processing of data.
What are the roles and responsibilities of a Tester In Big Data Applications?
The tester should be able to work with unstructured data and semi-structured data. They should also be able to work with structured data in the data warehouse or the source RDBMS.
Since the schema may change as the application evolves, the software tester should be able to work with a changing schema.
Since the data can come from variety of data sources and differ in structure, they should be able to develop the structure themselves based on their knowledge of the source.
This may require them to work with the development teams and also with the business users to understand the data.
In general applications the testers can use a sampling strategy when testing manually or an exhaustive verification strategy when using an automation tool. However in case of big data applications since the data set is huge even extracting a sample which represents the data set accurately, may be a challenge.
Testers may have to work with the business and development team and may have to research the problem domain before coming up with a strategy
Testers will have to be innovate in order to come up with techniques and utilities that will provide adequate test coverage while maintaining high test productivity.
Testers should know how to work with systems like Hadoop, HDFS. In some organizations, they may also be required to have or gain basic knowledge of setting up the systems.
Testers may be required to have knowledge of Hive QL and Pig Latin. They may also be called upon to write MapReduce programs in order to ensure complete testing of the application.
Testing of big data application requires significant technical skills and there is a huge demand for tester who possess these skills.
What are the key Attributes of Hadoop?
Redundant and reliable
Easy to program distributed apps
Runs on commodity hardware
What is Query Surge's architecture?
Query Surge Architecture consists of the following components:
Tomcat - The Query Surge Application Server
The Query Surge Database (MySQL)
Query Surge Agents – At least one has to be deployed
Query Surge Execution API, which is optional.