Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

Some challenges in big data include defining test strategies for structured and unstructured data validation, setting up an optimal test environment, working with non-relational database, and performing non-functional testing. These challenges cause poor quality of data in production, delayed implementation, and increased cost.

Big Data can be described by three “V”: Volume, Variety, and Velocity. In other words, you have to process an enormous amount of data of various formats at high speed. The processing of Big Data, and, therefore it’s software testing process, might be split into 3 basic components. The process is illustrated below by an example based on the open source Apache Hadoop software framework:

• Loading the initial data into the HDFS (Hadoop Distributed File System)
• Execution of Map-Reduce operations
• Rolling out the output results from the HDFS

The quality of the data is of equal importance to the value of getting the data transformed quickly. A validation plan must be put into place to ensure the accuracy of the data being consumed. Validations:

• Checking of required business logic on standalone unit and then on the set of units;
• Validating the Map-Reduce process to ensure that the “key-value” pair is generated correctly;
• Checking the aggregation and consolidation of data after performing “reduce” operation;
• Comparing the output data with initial files to make sure that output file was generated and its format meets all the requirements.

If you apply the right test strategies and follow best practices, you will improve Big Data testing quality, which will help to identify defects in early stages of the process and reduce overall cost. ETL and Testing Big Data with Hadoop

A traditional ETL (Extract, Transform, Load) process extracts data from multiple sources, then cleanses, formats, and loads it into a data warehouse for analysis. When the source data sets are large, fast, and unstructured, traditional ETL can become the bottleneck, because it is too complex to develop, too expensive to operate, and takes too much time to execute. Apache Hadoop is an open source distributed software platform for storing and processing data. It runs on a cluster of industry-standard servers configured with direct-attached storage. Using Hadoop, you can store petabytes of data reliably on tens of thousands of servers while scaling performance cost-effectively by just adding nodes to the cluster. The plan of the parthenon for instance is ultimately derived from that of a normal megaron with two columns in term paper writer antis, at either end. The newest wave of big data is generating new opportunities and new challenges for businesses across every industry. The challenge of quality data integration is one of the most urgent issues facing companies. Apache Hadoop provides a scalable platform for ingesting big data and preparing it for analysis. Using Hadoop to offload the traditional ETL processes can reduce time to analysis by days and weeks.