The term ‘Big Data’ is derived from the existence of huge amount of data. It illustrates very large volume of data, both Structured and Unstructured. Big data is a challenging terminology for both Data Scientists and Skilled IT professional to study, analyze and manage huge amount of data. There are some essential challenges which need to be managed such as data analysis, data capture, data curation, search, transfer, storage, sharing, visualization, querying, update and information privacy. It can be analyzed in order to deliver better decisions by using strategic analysis which may help in improving business processes.
Big Data Testing Strategy
In order to test big data application, the prominent verification of data processing is more important than testing the features of software product. The performance and functional testing are the two key testing methodologies. While testing, QA engineers verifies the processing of terabytes of data by using commodity cluster and other interconnected components. It requires high level of proficiency in testing skills to track and understand each process.
Data quality is the most crucial factor in big data testing. Before initiating the testing process, it is important to examine the data quality through database testing methodology, used to ensure various characteristics like conformity, accuracy, duplication, consistency, data completeness, etc.
Testing Steps In Verifying Big Data Applications
Below given are some of the activities that may be carried out in order to test the big data applications.
Data Staging Validation: This first step in big data testing involves pre-Hadoop process validation. In this phase, data from different sources such as RDBMS, social media, etc. is validated to ensure correct execution of data in the system. There is also a comparison between data and its verification to ensure that data extracted from right source.
Map Reduce Validation:The second step of validation is MapReduce. In this phase, tester validates the business logic by running at multiple number of nodes, so that Data Aggregation rules is successfully implemented, and key value pairs are properly generated in order to ensure validation.
Output Validation Phase:One of the last steps/stages in the direction of testing the big data application is Output Validation Phase. In this phase, output data files are generated and prepared to move in EDW (Enterprise Data Warehouse). Primary activities carried out in this phase involves the evaluation of transform rules to verify and validate their perfect implementation, checking data integrity & data load in the system and to ensure no data corruption while comparing targeted data with HDFS file system data.
Architecture Testing
Architecture testing is a crucial process that ensures the success of big data project. The architecture and its design are important to accomplish quality goals otherwise improper design and architecture may lead to performance degradation or system failure.
This also includes job completion time, memory utilization, data throughput and system metrics. The major aim is to test system failure, and verifying data processing.
Big Data Testing Tools
Tool for Validation of Pre-Hadoop Processing:-
Apache Fume: It is a reliable and efficient tool or application for transforming large number of data into HDFS.
Apache Sqoop:It is a tool used to transfer bulk data between Hadoop and relational databases.
Hive:With Apache Hive data warehouse, it is much easier to read, write and manage huge data sets in the distributed storage using SQL.
Pig :It provides alternate to SQL. It is also called Pig Latin, which is used as query data stored in HDFS.
NoSQL :All the Hadoop clusters not uses Hbase or HDFS. Some uses their own mechanisms to store data. It enables to store and retrieve data using NoSQL features.
Lucene/Solr :It is an open-source tool which is used for indexing large number of blocks of unstructured text in Lucene.
Testing Tools for Reporting Hadoop Processes:-
MRUnit: Unit Testing for MR Jobs
Local Job Runner Testing: Running MR jobs on single JVM Machine
Pseudo Distributed Testing.
Full Integration Testing.
Benefits of Big Data Testing
Some of the business benefits of big data testing are:
Business Decisions: The comprehensive data quality analysis is used to maintain the data quality, useful in making valuable business decision.
Reduce Time to Market:The customizable automation scripting templates like MapReduce and Pig Latin analyze filter conditions more effectively. Hence, it saves efforts and reduces time to market.
Seamless Integration:Outcomes of detailed study of current and new data requirements are applied in data acquisition, data migration and data integration testing methods to ensures seamless integration.
Quality Cost: : It reduces the total cost of quality, overall data analysis and migration testing that improves productivity and helps to keep maintaining quality measures.
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