Implement Big Data Testing in Order to Successfully Generate Analytics. This Blog is ideal for software testers and anyone else who wants to understand big data testing.

1. Strategies of Big Data Testing
Today, companies all over find themselves inundated with data. This big complex data
gives these companies a hard time. They find it difficult to process, manage, and
analyze it for their progress. For extracting the maximum value out of it, they require a
dynamic ​Big Data testing​ mechanism in place.
Data is being generated at a rapid pace. In the near future, it will only expand further
with the number of connected devices crossing 41.6 billion by 2025. Before moving onto
the various ​Big Data testing​ methods, it is essential to get clarity on what actually Big
Data entails.
According to Gartner, the high-volume, high variety, or velocity assets of information are
termed as Big Data. It demands advanced and innovative processing mechanisms that
enable organizations to derive valuable insights and, as a consequence, improve its
products and services.
Big companies like Facebook and Twitter generate up to 4 Petabytes and 12 Terabytes
of data per day. It is generated as structured, unstructured, and semi-structured data.
Examples of structured data include databases, data warehouses, and enterprise
systems like CRM, ERP, etc. Unstructured ones include images, videos, mp3 files,
among many. Semi-structured data are those not rigidly organized and contain various
tags like XML, CSV, and JSON.
Big Data testing ​primarily refers to the process of validating the major functionalities of
Big Data applications. Nowadays, businesses are eager to avail of the ​Big Data testing
and ​QA testing services​ of a ​software testing company​. Nevertheless, the immense
complexity of Big Data makes its testing dramatically different from normal software
testing.
Big Data testing​ - What is it
The defining features of Big Data are:
● Volume, that is, the size of the data.
● Velocity, that is, the speed at which data is produced.
● Variety, that is, the different kinds of data produced.
● Veracity, that is, the data’s trustworthiness.
● Value, that is, how Big Data can be transformed into valuable business insight.

2. Methods of ​Big Data Testing
There are several different techniques used for testing Big Data. These testing
strategies cannot be accomplished without the following prerequisites:
1. Highly skilled and qualified ​software testing company​ experts.
2. Powerful automation testing tools.
3. Readily available processes and mechanisms that will work to validate the
movement of data.
Given below are ​Big Data testing​ techniques used to test a particular functionality of
Big Data.
● Data Analytics and Visualization testing test its volume.
● Its velocity is measured through migration and source extraction testing.
● Its variety is validated by performance and security testing.
● Its veracity is validated by Big Data ecosystem testing.
Major components of ​Big Data testing​ strategies.
● Data staging process
● MapReduce validation
● Output validation
1. Data staging process
Also known as the pre-Hadoop stage, this​ Big Data testing​ stage starts with process
validation. Data verification is an essential part that is undertaken during this stage.
There is a need to ascertain that authentic data is being collected from different
sources. The data should not be corrupt and inaccurate. Only after the data’s
authenticity is established, can it be put into a machine. The data is stored in a
particular location. Source data needs to be matched to the added data in the machine
through comparison and validation.
Tools like Datameer, Talent, and Informatica are used at this stage.
2. MapReduce validation
This stage consists of two different functions. As the name suggests, those two are the
Map function and the Reduce function. When performing the Map task, Hadoop

3. receives and converts a dataset into another. During this process, the different
components of the dataset are separated into value pairs.
The outcome from the Map task is received as input during the Reduce task. All the
separate value pairs are combined into even smaller pairs at the end of this task. Both
Map and Reduce tasks are performed consecutively. MapReduce process makes data
validation complete.
3. Output validation
During this process, the output file is obtained and loaded into the output folder. At the
end of this task, the target data and file data are compared to prevent chances of data
corruption. It is done by moving the output files to the EDW, that is, Enterprise Data
Warehouse.
System architecture testing
Architecture testing is indispensable to a successful Big Data project. Hadoop
processes huge volumes of data. Its poor architecture may lower its performance;
consequently, it will not be able to accomplish the requirements. Hence, Performance
and Failover test services like testing job completion time, data throughput, memory
utilization, etc. should be done in the environment of Hadoop.
Performance testing
Performance testing involves the following:
1. Data ingestion: The tester verifies the speed at which the system consumes the
data from different sources. It involves identifying a different message that can be
processed by the queue in a specific time period. Additionally, it also involves the
pace at which data can be inserted into an existing data store. Example, Mongo
or Cassandra database.
2. Processing of the data: The speed at which MapReduce tasks are executed is
verified during data processing. It also consists of testing the speed of data
processing when the existing datastore is already filled with numerous data sets.
An example can be running MapReduce tasks on the HDFS.
3. Testing the performance of individual components: Big Data systems comprise
various components. For their effective working, it is essential to test each

4. component individually. For example, the performance of MapReduce tasks,
search, query performance, etc. should be checked in isolation.
Big Data testing ​Environment Needs
The test environment differs according to the application being tested. ​Big Data testing
demands a test environment that comprises the following:
● Adequate storage space, along with the ability to process huge volumes of data.
● It should be resource-intensive with minimal CPU and memory consumption to
keep its performance high.
● Clusters having distributed nodes and data is another requirement for the testing
environment.
Hence, we see that the characteristics of Big Data demand a testing process that is
radically different from conventional software testing. It, therefore, requires highly skilled
QA testing services​ experts to effectively carry out the testing of its each and every
functionality.
Automation testing tools for Big Data
Big Data testing​ is conducted using multiple automation testing tools, all of which
integrate well with Hadoop, MongoDB, AWS, etc. All of the tools need to have certain
features like scalability, dependability, economic feasibility, and a robust reporting
functionality. Some of the commonly used ones include the Hadoop Distributed File
System (HDFS), MapReduce, HiveQL, HBase, and Pig Latin.
Conclusion:
The importance of Big Data remains undeniable for companies worldwide. The key
benefits of a successful Big Data processing and analysis include optimized
decision-making and enhanced financial performance. It plays a big role in serving
customers better and forging a long term relationship with them. With more and more
businesses depending on Big Data analysis, we can only hope to see more of its robust
testing techniques being developed in the future.