If you're looking for Big Data Hadoop Testing Interview Questions for Experienced or Freshers, you are at the right place. There are a lot of opportunities from many reputed companies in the world. According to research Hadoop Market is Expected to Reach $84.6 Billion, Globally, by 2023. So, You still have the opportunity to move ahead in your career in Hadoop Testing Analytics. Mindmajix offers Advanced Big data Hadoop Testing Interview Questions 2023 that helps you in cracking your interview & acquire a dream career as a Hadoop Testing Analyst.
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 the 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.
In the case of processing of the significant amount of data, performance, and functional testing is the primary key to performance. Testing is a validation of the data processing capability of the project and not the examination of the typical software features.
In Hadoop, engineers authenticate the processing of quantum of data used by the Hadoop cluster with supportive elements. Testing of Big data needs asks for extremely skilled professionals, as the handling is swift. Processing is three types namely Batch, Real-Time, & Interactive.
Along with processing capability, the quality of data is an essential factor while testing big data. Before testing, it is obligatory to ensure the data quality, which will be part of the examination of the database. It involves the inspection of various properties like conformity, perfection, repetition, reliability, validity, completeness of data, etc.
The initial step in the validation, which engages in process verification. Data from a different source like social media, RDBMS, etc. are validated, so that accurate uploaded data to the system. We should then compare the data source with the uploaded data into HDFS to ensure that both of them match. Lastly, we should validate that the correct data has been pulled, and uploaded into a specific HDFS. There are many tools available, e.g., Talend, Datameer, which are mostly used for validation of data staging.
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MapReduce is the second phase of the validation process of Big Data testing. This stage involves the developer verifying the validation of the logic of business on every single systemic node and validating the data after executing on all the nodes, determining that:
Proper Functioning, of Map-Reduce.
Rules for Data segregation are being implemented.
Pairing & Creation of Key-value.
Correct Verification of data following the completion of Map Reduce.
Third and the last phase in the testing of bog data is the validation of output. Output files of the output are created & ready for being uploaded on EDW (warehouse at an enterprise level), or additional arrangements based on need. The third stage consists of the following activities.
Assessing the rules for transformation whether they are applied correctly
Assessing the integration of data and successful loading of the data into the specific HDFS.
Assessing that the data is not corrupt by analyzing the downloaded data from HDFS & the source data uploaded.
This pattern of testing is to process a vast amount of data extremely resources intensive. That is why testing the architecture 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 a proper performance in any Hadoop environment.
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
The developer validates how fast the system is consuming the data from different sources. Testing involves the identification process of multiple messages that are being processed by a queue within a specific frame of time. It also consists of how fast the data gets into a particular data store, e.g., the rate of insertion into the Cassandra & Mongo database.
It involves validating the rate at which map-reduce tasks are performed. It also consists of data testing, which can be processed in separation when the primary store is full of data sets. E.g., Map-Reduce tasks running on a specific HDFS.
Systems designed with multiple elements for processing a large amount of data needs to be tested with every single of these elements in isolation. E.g., how quickly the message is being consumed & indexed, MapReduce jobs, search, query performances, etc.
The method of testing the performance of the application constitutes the validation of a 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
Different parameters need to be confirmed while performance testing which is as follows:
Data Storage which validates the data is being stored on various systemic nodes
Logs that confirm the production of commit logs.
Concurrency establishing the number of threads being performed for reading and write operation
Caching confirms the fine-tuning of "key cache” & "row cache" in settings of the cache.
Timeouts are establishing the magnitude of query timeout.
Parameters of JVM are confirming algorithms of GC collection, heap size, and much more.
Map-reduce which suggests merging, and much more.
Message queue, which confirms the size, message rate, etc
Test Environment depends on the nature of the application being tested. For testing Big data, the environment should cover:
Adequate space is available for processing after a significant storage amount of test data
Data on the scattered Cluster.
Minimum memory and CPU utilization for maximizing performance
Developer faces more structured data in the case of conventional database testing as compared to testing of Big data which involves both structured and unstructured data.
Methods for testing are time-tested and well defined as compared to an examination of big data, which requires R&D Efforts too.
Developers can select whether to go for "Sampling" or manual by "Exhaustive Validation" strategy with the help of an automation tool.
A conventional way of a testing database does not need specialized environments due to its limited size whereas in the case of big data needs a specific testing environment.
The validating tool needed in traditional database testing are excel based on macros or automotive tools with User Interface, whereas testing big data is enlarged without having specific and definitive tools.
Tools required for conventional testing are very simple and do not require any specialized skills whereas big data tester need to be specially trained, and updations are needed more often as it is still in its nascent stage.
|NoSQL:||Cassandra, CouchDB, DatabasesMongoDB, Redis, HBase. ZooKeeper|
|MapReduce:||Hadoop, Pig, Hive, Cascading, Kafka, Oozie, S4, Flume, MapR|
|Servers:||Elastic, EC2, Heroku|
|Processing||Mechanical Turk, R, Yahoo! BigSheets.|
Organizational Data, which is growing every data, ask for automation, for which the test of Big Data needs a highly skilled developer. Sadly, there are no tools capable of handling unpredictable issues that occur during the validation process. Lots of Focus on R&D is still going on.
Virtualization is an essential stage in testing Big Data. The Latency of the virtual machine generates issues with timing. Management of images is not hassle-free too.
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.
Big data is a combination of varied technologies. Each of its sub-elements belongs to 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.
Query Surge is one of the solutions for Big Data testing. It ensures the quality of data quality and the shared data testing method that detects bad data while testing and provides an excellent view of the health of data. It makes sure that the data extracted from the sources stay intact on the target by examining and pinpointing the differences in the Big Data wherever necessary.
Query Surge helps us to automate the efforts made by us manually in the testing of Big Data. It offers to test across diverse platforms available like Hadoop, Teradata, MongoDB, Oracle, Microsoft, IBM, Cloudera, Amazon, HortonWorks, MapR, DataStax, and other Hadoop vendors like Excel, flat files, XML, etc.
Enhancing Testing speeds by more than thousands of times while at the same time offering the coverage of entire data.
Delivering Continuously – Query Surge integrates DevOps solution for almost all Build, QA software for management, ETL.
It also provides automated reports by email with dashboards stating the health of data.
Providing excellent Return on the Investments (ROI), as high as 1,500%
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.
The Query Surge Agent is the architectural element that executes queries against Source and Target data sources and getting the results to Query Surge.
For any Query Surge or a POC, only one agent is sufficient. For production deployment, it is dependent on several factors (Source/data source products / Target database / Hardware Source/ Targets are installed, the style of query scripting), which is best determined as we gain experience with Query Surge within our production environment.
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Query Surge has its inbuilt database, embedded in it. We need to lever the licensing of a database so that deploying Query Surge does not affect the organization currently has decided to use its services.
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
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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