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Understanding Big Data Testing

Today Big Data chunks are influencing our business decisions. So, there is a need to understanding Big Data and the need for Big Data Testing that can help us to not only make sense of the data we collect but also help us to achieve business success.

The steady growth in the number of connected devices has also given birth to huge volumes of data. A forecast from International Data Corporation (IDC) estimates that there will be 41.6 billion connected devices, or “things,” generating 79.4 zettabytes (ZB) of data in 2025.

What is Big Data Testing and why is it needed?

From this forecast by IDC, we can understand the amount of data that is going to come into our hands is only going to grow. When such a large amount of data is being generated, most companies are looking to aggregate and derive insights from it to improve their products and services. Data is being created, stored, retrieved, and analyzed each day and to do all of these efficiently, there is a need to implement Big Data testing in order to successfully generate analytics.

If we have to define Big Data testing, we can say that it is a procedure to validate the functionalities of Big Data applications. Validating these huge streams of data is not an easy task.  Various tools and techniques can aid this procedure. However, Big Data testing is nothing like testing software. Big Data can be understood using three vectors that are associated with it.

Volume: Big Data is always more than what a single machine can handle. Data that is being generated needs to be distributed across different machines, and this can be done effectively with the help of Hadoop. Hadoop is an open-source software framework that is mainly used for storing and distribution of data, and running applications on the hardware. With such vast volumes of data, the essential step is to distribute and store it.

Velocity: Another factor that is associated with Big Data is velocity. A connected device generates large amounts of data on a daily basis. One of the key concerns here is handling this data in real-time. This data has to be distributed effectively so that it can be further analyzed. The data that is generated needs to be checked for anomalies and other compromises to make proper distribution and this needs to be done constantly as data streams keep emerging.

Variety: The data streams that we receive are not always the same. They change depending upon the application. Most of the data that we receive is unstructured data, while some of it is structured or semi-structured. Big data is not just about large volumes or about emerging at a higher speed, but also about the diverse nature of data. This is how we can understand Big Data.

Understanding Big Data Testing Strategy

Now based on the three Vs we discussed earlier, we also have various testing methods that can be split into the following three categories:

Data Staging Process: When it comes to Big Data testing, we start with process validation. This first stage is also known as the pre-Hadoop stage. One important part is verifying the data that you receive from various sources before you can add it to a system or machine. During the data staging process, you can identify that the right data is collected and stored in the specified location. Source data and the data added to the machine has to be compared and validated whether it is a match or not.

MapReduce Validation: The term MapReduce distinctly refers to two separate tasks that Hadoop performs.  If we have to split the term and look at it then one part will be the Map and the other Reduce, which are two distinct functions. During the map task, it takes a dataset and converts it into another dataset. The individual elements are broken into value pairs or tuples. During the reduce task, the output derived is taken from the map as the input and integrate the data tuples into a smaller sets. Both these tasks are always performed in sequence.

Output Validation: Output Validation is generally the last stage of the Big Data testing process. This process mainly consists of extracting the output file and loading it on to the target output folder. Once this process of validation is completed, there is also a need to check for data corruption by comparing the target data with the file data.

Our need to make sense of the vast volumes of data determines the intelligence behind our business decisions. You need to have a strong team that can help you test your data streams and help you to use data analytics to influence your business success. eInfochips have over 25 years of experience of being the backbone of various successful businesses around the world,  and with a wide range big data services across the entire data lifecycle, can help you to you to correlate your data and achieve business success. Talk to our Big Data experts and know more about how we can help you.

Picture of Smishad Thomas

Smishad Thomas

Smishad Thomas is the Customer Experience Manager at eInfochips. He has over 10 years of experience into customer service and marketing. Smishad has completed his Masters in English Literature along with a degree in Corporate Communication.

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