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Big Data Testing: Different Types and Top Strategies
by Tech Addict Writer, BloggerIt is no secret that big data has come to play a crucial role in driving success for companies and businesses of all scales and sizes across the spectrum of industries. However, this growing importance of big data has also put the spotlight on big data testing, i.e. subjecting humongous collections of data to different tests to get a better idea of their usage, characteristics, etc. Big data testing is important because it helps drive better decisions, helps companies cut down losses and drive better revenue, and put together better strategies that align with the business goals. It can be defined as the procedure that involves examining and validating the functionality of the Big Data Applications. And, big data refers to the collection of a huge amount of data that traditional storage systems cannot handle.
To help you further understand how to leverage big data testing, let us start this deep dive with a list of the most crucial characteristics of big data and data formats:
- Velocity
- Variety
- Volume
- Veracity
Time to take a look at different data formats:
- Structured data: This type of data refers to highly organized data that can be easily retrieved via queries.
- Semi-structured data: This type of data follows no strict organization parameters and can include metadata, tags, etc. Examples of such data include JavaScript Object Notation (JSON), CSV, XML, etc.
- Unstructured data: Such data does not follow a format and can be challenging to store and retrieve.
Now, let us also look at the different types of big data testing one can use:
- Architecture testing: Aimed to help gauge how well the data has been organized, architecture testing is crucial for verifying the data performance as well as identifying problems and errors in the data that may not be performing as expected.
- Data ingestion testing: This type of testing, which is used to make sure that the data has been inserted appropriately, uses tools such as Kafka, Zookeeper, Flume, Sqoop, etc.
- Data process testing: This form of testing involves the use of tools such as Hive, Hadoop, Oozie, Pig, etc. to ensure that the business logic is indeed right.
- Data storage testing: Testing teams make use of tools such as HBase, HDFS, etc. to compare output data and warehouse data.
The best and most robust big data testing strategies are based on three
components. Allow us to walk you through them and the strategies they drive:
- Data validation: At this point in the testing process, the collected data is analyzed to make sure that it is accurate and not corrupted. To do that, i.e. validate the data, the collected data is sent through the Hadoop Distributed File System (HDFS).
- Process validation: Also known as Business Logic validation, process validation takes a closer look at the business logic for various nodes at each node point. The testing team has to back up the process as well as the key-value pair generation.
- Output validation: This stage of the testing process seeks to verify if there are any distortions in the data. This is done by loading data downstream.
There you have it ladies and gentlemen, a quick but in-depth overview of what big data testing is all about. It is a beneficial endeavor — one that stands to change how companies can leverage the abundance of data they have access to. Just remember, with the right strategy and expert support, testing big data applications can be as easy as ABC.
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About Tech Addict 
Writer, Blogger
5 connections, 0 recommendations, 61 honor points.Joined APSense since, November 22nd, 2016, From Vododara, India.
Created on May 17th 2022 05:31. Viewed 156 times.
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