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Big Data Testing

The world is moving at high speed towards Big Data investments. Big Data are means to take accurate business decisions at strategic as well as transactional layer. Quality of entire Big Data architecture (Data, Application, Processes) become extremely important to take correct decisions and subsequent actions.


Big data testing involves functional and non-functional testing are required along with strong test data and test environment management to ensure that the data from varied sources is processed error free and is of good quality to perform analysis. Following diagram highlights testing type and needs at various points in big data architecture.


Functional Testing of Big Data

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Cost of correction of a software bug multiplies as it passes undetected through various stages of big data implementation cycle. ....................................................
 
Since big data deal with huge chunk of data, there are high chances of having bad data and data quality issues at each stage of the process. Functional Testing should be performed at each of the four phases of Big data processing to ensure that data is getting processed without any errors.

(I) Validation of pre-Hadoop processing
(II) Validation of Hadoop Map Reduce process data output
(III) Validation of data extract, and load into EDW
(IV) Validation of Reports
 

Non Functional Testing of Big Data

During these phases of Big data processing the three dimensions or characteristics of Big data i.e. volume, variety and velocity are validated to ensure there are no data quality defects and no performance issues.

Volume: The amount of data created both inside corporations and outside the corporations via the web, mobile devices, IT infrastructure, and other sources is increasing exponentially each year

Variety: The variety of data types is increasing, namely unstructured text-based data and semi- structured data like social media data, location- based data, and log-file data.

Velocity: The speed at which new data is being created – and the need for real-time analytics to derive business value from it -- is increasing thanks to digitization of transactions, mobile computing and the sheer number of internet and mobile device users. Data speed needs to be considered when implementing any Big data appliance to overcome performance problems.

Performance Testing: Any Big data project involves in processing huge volumes of s truc tured and unstructured d ata and is processed across multiple nodes to complete the job in less time. At times because of bad architecture and poorly designed code, performance is degraded. If the performance is not meeting the SLA, the purpose of setting up Hadoop and other Big data technologies is lost. Hence, performance testing plays a key role in any Big data project due to huge volume of data and complex architecture.

Failover Testing: Failover testing is an important focus area in Big data implementations with the objective of validating the recovery process and to ensure the data processing happens seamlessly when switched to other data nodes.

Automation: The Big data regression test suite will be used multiple times as the database will be periodically updated. Hence an automated regression test suite should be built to use it after reach release. This will save a lot of time during Big data validations.

AUDITime is equipped to service any big data project with its suite of services including Functional Testing, Performance Testing, Failover testing and automation of testing suite.
 

 
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