Big Data Testing and It’s Role

Words, Numbers, Measurements, Calculations, Information – we are surrounded by all of it.  All of this and much more is collectively known as – DATA. It has the ability to be transformed into some specific information required pertaining to a particular context. Data, I believe is the crux of everything from – Social Media to Businesses to Research to basic stuff like searching something on Google, the clickstreams. Everything that we are doing is being stored somewhere, which means very large quantities of data are being collected and processed on a daily basis. | Big Data Testing and It’s Role

These large volumes of data – refined, complex, or raw and in all the forms and types from different data sources which cannot be processed using traditional systems like – RDBMS, etc. are called “Big Data”. Hadoop Framework is used to support the processing of such large data sets in a distributed computing environment. There is so much data being pushed and pulled by sources around the world in the name of analytics.

What is Big Data Testing?

As we know Big Data comprises large volumes of data, data sets that cannot be assessed and processed using traditional methods and computation techniques. It needs several tools, and frameworks for processing, and testing it. Special test environments are required due to large data sizes and files. There are so many aspects of data that need to be validated and verified and processed like – the quality of data, the density of data, and the accuracy of the processed data output, etc. before it is deemed fit for use or to see if it is bringing anything valuable to the table.

Performance Testing and Functional Testing play a very important role in the verification of data, as we are not testing a product or functionality here and this much data to deal with and verify and structure it could inundate the QA’s.

Hence, there are particular steps that need to be followed in order to perform this unconventional setup of testing approach –

1. Data Staging Validation – Data from various sources is validated to ensure that we are collecting and pulling the right data and then the source data is compared to the data that we are pushing into the Hadoop system and making sure that the correct data is being extracted and loaded into Hadoop Distributed File System (HDFS).
2. Map Reduce Validation – This is done by the QA’s to verify and validate the business logic applied at each node and ensure a smooth run.
3. Output Validation Phase – This is the stage where we want to validate the output. The output data files are generated, data integrity is checked for and then the files are loaded into the target system. Also, we check for any data redundancy or corruption here.

Having these three steps as the foundation of our Testing, QA’s then go on to perform several types of testing techniques like –

1. Performance Testing – As we are dealing with large volumes of data, it is important to ensure that this in any way does not impact the speed at which data is being processed. How the system is performing as a whole. We need to check how the data is being stored, whether the key-value pairs are being generated successfully, and whether there is proper sorting and merging of data if there are any queries or connection timeouts happening.
2. Architecture Testing – Architecture testing is done to ensure that the system has been designed well and meets all of our requirements. 
3. Functional Testing – Functional Testing is done to ensure that the system is compliant with the specific requirements and if the system is doing exactly what it was built to do. 

Big Data can be characterized into 3 substantial factors –

1. VOLUME – Big Data essentially is high volumes of unfiltered, unprocessed, raw data. It could be valuable or invaluable at this point in time.
2. VELOCITY – It is the rate at which the data is received and processed. With so much technology taking over the world, not just with respect to businesses, and data feeds but also different (IoT) devices that are continuously and consistently collecting data. All this requires real-time evaluation and action.
3. VARIETY – Considering that the data is being sourced from different and all kinds of sources, a variety of data is involved that has to be acted upon and processed. 

Other than these 3 main characteristics that define data, there are 2 other important factors that tell us more about what we are dealing with –

1. VALUE – With such high volumes of data, we need to be sure if the data being processed even holds some value or not. Data in today’s world is money, so we need to ensure how reliable and valuable the data is.
2. VERACITY – It again goes on to show us the quality of data. With so many sources around the world pushing data, it becomes really difficult to assess its quality and clearly define whether the data is valuable or poor quality.

Importance of Big Data –

Data in today’s world holds so much value, irrespective of the sources that it is being collected from, and rightly so because we can use this data to get hold of a lot of information and find out solutions to so many pre-occurring problems like –

• How to make decisions smartly determining the issues and causes that have led to failures previously.
• Assessing real-time situations and responding to the need on the go due to quickly analyzing data and reacting to it.
• Detecting potential threat situations like – hacking, fraud, etc.
• Integrating data from different sources and building operational and business management strategies on the basis of data patterns.
• Profiting from the data analytics by knowing consumer pulse and acting on it.
• It helps unlock potential information required to get an insight into the future growth of businesses and industries.

Problems with Big Data Testing 

• Test Script Creation – Test Script creation can be quite a challenging task as there is so much data and accuracy required, that it gets difficult to narrow it down to the scenario and script level.
• Technology – There are so many parameters of Technology associated with Big Data that it becomes difficult to bring them all together and utilize the full potential of it.
• Test Environment – Special Test Environments are required to accommodate such large data volumes and file systems.
• Automation – Automation Testing for Big Data can be a bit tricky as it involves so many unforeseeable scenarios that cannot be chalked down initially at the scripting level.
• Large Data – There is so much data being processed every second, it becomes really difficult to process it at a very fast rate.

Tools Used for Big Data Testing 

• For Map Reduce Stage – Hadoop, Hive, Pig, Cascading, Oozie, Kafka, S4, MapR, Flume
• For Storage – S3, HDFS
• Servers – Elastic, Heroku, Google App Engine, EC2
• For Processing – R, Yahoo! Pipes, Mechanical Turk, BigSheets, Datameer.

CONCLUSION

With so much around the world being collected every day from different sources and devices and platforms, it becomes essential that it is processed quickly and accurately to identify the unforeseeable as well as the foreseeable. Giants like Amazon, Ikea, etc. already have a strong foothold in the field of data.