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Key Considerations While Testing Big Data

Key Considerations While Testing Big Data

Nowadays, Big Data is present everywhere with every realm utilizing it. In the past few years, the inquisitiveness about what is Big Data has been soaring. Let’s unpack some facts related to Big Data.

The American business magazine, Forbes recently stated that 80% of the users scroll YouTube, watch 4.15 million videos every minute, share 46,740 images on Instagram every second, Twitter has 456,000 tweets by every instant, and Facebook is accompanied with 293,000 statuses and 510,000 comments every moment.

By looking at the figures, we can image the huge amount of data that is generated with such events. This continuous formation of information with the help of business applications, telecom, social media, and several other domains is heading to the creation of Big Data.

Peculiarities of Big Data

We reside in a world that is reaching 3 zettabytes (ZB) of information. To put that in viewpoint, if a GB was considered brick, with a ZB, we could establish over 250 Great Walls of China.  All this can happen, due to a high rate of user-produced data, like that 100TB (1 TB= 1 large PC hard drive) of information shared to Facebook on a regular basis.

A lot of time, Big Data is the solution to varied and complex business issuers and can provide solutions immediately. The 3Vs (volume, velocity, and variety) that describe it make testing necessitate dedicated devices and skilled staffs.

The wave of data that keeps coming in is categorized by the 3V’s:


Volume is possibly the most related V in terms of big data. You connect with unintelligible choices of information. In YouTube, nearly 300 hours worth of videos is posted since almost everyone has a smartphone nowadays.


Velocity is considered about the determination of the quantity of data that is flowing in. Let’s take the example of Facebook. Consider at the number of images it has to store, execute and then repossess. In the original phases of information attaining, companies practiced doing batch processing of the data that’s flowing in.


Here all sorts of information are deemed- unstructured, semi-structured and unstructured. You will have pictures, sensor data tweets, encrypted packets and abundance of such stuff. Information is not something not offered to you in a database anymore. As the information data comes in the structured setup, possessions become stress-free, but when they come in the kind of pictures, audio recordings, books, videos, geospatial information, posts & comments, presentations, email messages, brochures, tweets, and ECG strips, you should consider the data as overflowing and unstructured.

Big Data Analytics

As we are now acquainted with Big Data and its characteristics, let take an example of leading coffeehouse chain, Starbucks to comprehend how they are making use of Big Data.

The global media organization, Forbes mentioned an article which cited how Starbucks made use of Big Data to examine the choices of their consumers to increase and customize their experience. Additionally, they observed their member’s coffee purchasing habits along with their favored drinks to what time of day they are generally ordering. Thus, even when the individuals visit a “new” Starbucks site, that store’s point-of-sale arrangement is able to recognize the consumer via their smartphone and offer their preferred coffee with a suggestion. Further to this, powered on ordering choices, their app will recommend new products that the consumers might be fascinated in trying. Thus, Big Data Analytics is derived from this concept.

What Is Big Data Testing?

This testing is stated as testing of Big Data applications. Apart from this, big data is an assortment comprised of huge datasets which cannot perform execution while practicing traditional computing procedures. Testing of such datasets comprises several techniques, equipment, and frameworks to operate. Big data pertains to the formation of the data, its analysis, retrieval and also its storage that is notable with respect to rapidity, volume, and variation.

Big Data Testing Strategy

The testing of big data application is just not confined to the testing of the individual characteristics of the software product but incline more towards the certification of the data processing. For big data testing, functional testing and performance are the dominant aspects.

While executing Big Data testing, Quality Assurance engineers authenticate the effective processing of terabytes of information with the help of commodity cluster and other helpful considerations. The big data testing necessitates a high level of testing services as the process is rapid.

Before experimenting on the application, it is essential to verify the quality of information and should be deemed as a part of database testing. Additionally, it comprises verifying several features like validity, conformity, accuracy, data completeness, consistency, duplication, etc.

Parameters to Be Deemed While Testing Big Data Application

The big data is defined through the stated three Vs, you ought to know how to process all this information via its numerous formats at a high pace. This procedure can be divided into three basic constituents. To be effective, testers will have to be vigilant of these constituents.

•    Information authentication: Comprehensibly, this is one of the maximum vital components of information collection. In order to safeguard that the information is precise and not ruined, it is vital that it is authenticated. For such purpose, the sources must be verified. The data procured is authenticated against actual business necessities.

•    Procedure authentication: Once the information and source are coordinated, they will be forced to the right site. This would be the business logic authentication or procedure validation, where the tester will authenticate the business logic, lump by lump, and then certify it against distinct junctions. By the means of the “reduce” process, the merging and combination of data are verified out.

•    Output authentication: Output authentication is the next vital constituent. Here, the produced data is filled into the downstream process (this could be a data repository) and the data goes via analysis and further execution. This is then further verified to make sure the information is not inaccurate by associating the HDFS file system with aimed information. Architecture testing is another vital component of big data testing, as attaining poor architecture will make the whole hard work go to surplus. It is also vital to safeguard that there is no information corruption and relate the HDFS file system information with aimed UI or commercial intelligence method.

Big Data Applications

Big Data applications are revolutionized every now and then in a number of fields. Below are some of the following realms where Big Data is practiced:-

•    Realm of Entertainment: Amazon practices Big Data to execute shows and movie endorsements to their users. Not just this, Netflix also practices Big Data to Enhance Consumer Experience.

Big Data offers Netflix the capability to serve the content the consumer wants to witness when the consumer urges it.

•    Government: A very exciting practice of Big Data is in the province of politics to examine patterns and encourage election outputs. Cambridge Analytica Ltd. is one such organization which totally drives on data to morph audience behavior and acts a major role in the electoral procedure.

•    Automobile: One of the leading automobile company, Rolls Royce has cherished Big Data by placing hundreds of sensors into its engines and impulsion processes, which register every tiny detail about their procedure. The alterations in data in real-time are registered to engineers who will finalize the best course of action like the scheduling maintenance or shipping engineering groups should the issue necessitate it.

•    Driver-less Cars: The well-known Google’s driver-less cars garner about one gigabyte of information per second. Such experiments necessitate more and more information for their effective implementation.

•    Insurance: Practices Big data to anticipate accidents, price their products and illness consequently.

•    Knowledge field: Picking for big data-based technology as an education tool in lieu of traditional lecture approaches, which increased the learning of scholars along with assisted the teacher to analyze their performance in a better way.

All in All,

Morphing information with intelligence is a prime concern. Big data is important to any organization’s decision-making approach and therefore it’s mandatory to not ignore the importance of testing big data as well, it is not even possible to begin proclaiming the significance of arming yourself with dependable information.

Big data processing is a very promising realm in today’s complex business ambiance. Implementing, the right dose of test policies and accompanying best strategies will safeguard qualitative software testing. With the help of big data, the notion is to distinguish and classify the flaws in the early phases of testing and resolve them. Additionally, this assists in charges lessening and better recognition of organizations targets. By the means of this procedure, the issues that testers witness during software testing are determined because the testing tactics are extracted by the data.

Sanjay Kumar

Mr. Sanjay is no new to the marketing world where his work speaks it all. As a certified inbound marketer, his contributions shine on web pages helping startups and established firm to acquire their motives and gains. Business wisdom put into practice is what is the personality is known about, making him the first pick of many.

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