Big Data Testing is a process that involves looking over and confirming the functionality of Big Data Applications. Big Data is the term for a collection of enormous amounts of data that traditional storage systems cannot handle. There are several areas in it where its testing strategy is required. There are various types of testing in Big Data projects, such as Database testing, Infrastructure, Performance Testing, and Functional testing. It is defined as a large volume of data, structured or unstructured. Data may exist in any format, like flat files, images, videos, etc. Its primary characteristics are three V's - Volume, Velocity, and Variety where volume represents the size of the data collected from various sources like sensors and transactions, velocity is described as the speed (handle and process rates), and variety represents the formats of data. Learn more about Continuous Load Testing in this insight. The primary example of it is E-commerce sites such as Amazon, Flipkart, Snapdeal, and another E-commerce site that have millions of visitors and products.
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This data is collected from multiple sources such as CSV, sensors, logs, social media, etc., and further stored in HDFS. In this testing, the primary motive is to verify whether the data is adequately extracted and correctly loaded into HDFS. Tester has to ensure that the data properly ingests according to the defined schema and also has to verify that there is no data corruption. The tester validates the correctness of data by taking some little sample source data and, after ingestion, compares both source data and ingested data with each other. And further, data is loaded into HDFS into desired locations. Tools - Apache Zookeeper, Kafka, Sqoop, Flume
In this type of testing, the primary focus is on aggregated data. Whenever the ingested data processes, validate whether the business logic is implemented correctly or not. And further, validate it by comparing the output files with the input files. Tools - Hadoop, Hive, Pig, Oozie
The output is stored in HDFS or any other warehouse. The tester verifies the output data is correctly loaded into the warehouse by comparing the output data with the warehouse data. Tools - HDFS, HBase
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Steps to adopt its testing strategies are listed below:
The revolution in it is starting to transform how companies organize, operate, manage talent, and create value. Source- Big Data
The top 5 benefits are:
It plays a vital role in its Systems. If its systems are not appropriately tested, it will affect business, and it will also become tough to understand the error, the cause of the failure, and where it occurs. Due to this, finding the solution to the problem also becomes difficult. If its Testing is performed correctly, it will prevent wasting resources in the future.
The below mentioned are the best practices of its Testing:
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Big Data is the trend that is revolutionizing society and its organizations due to the capabilities it provides to take advantage of a wide variety of data, in large volumes and with speed. However, many organizations are taking their first steps to incorporate it into their processes. Therefore, we compiled some best recommendations for its Testing Tools starting in the world of data.
