Big data is a challenging terminology which is defined as the large amount of data is in both structured and unstructured form, and even experts find this terminology challenging to manage this data. Big data testing actually does not involves the testing of the big data applications or system, rather this testing technique is used to evaluate and assess the processing of these huge amount of data including functional and performance aspect of the application.
There are some major prospects that make big data testing produce impressive and goal-oriented methodology:
Need for Big Data Testing Checklist:
Problems and future hindrances occur due to undefined points or some uncovered portions which are skipped without following a defined pattern. Checklist is an effective way to execute functionality and features in a correct way. By integrating this procedure, a technique will produce imperative results because of its systematic methodology. It will also help to improvise to process organic data, good data infrastructure, structured prototyping and modelling.
There are some major prospects that make big data testing produce impressive and goal-oriented methodology:
Integration testing: This checklist helps to ensure ETL workflows execution as per the schedule with accurate dependencies. Integration testing must validates successful execution of data loading workflows. Extensive focus on database tables and their records. Checking for errors to correct major issues and verifying the work and time variations.
Performance and Scalability testing: It is obvious that when the volume of data increases, ETL execution time will also hike and performance will be affected. If we take care about these things, performance and scalability will increase; when load of the database with expected volumes helps to ensure that volume of data is packed by ETL process, by comparing loading time, validate query performance for large volume of data, match the timings and apply simple queries to validate large amount of data.
Unit testing: In unit testing, checking of support data staging, duplicity of values, correction of surrogate keys, data-type constraints, data loading status, data truncation, data types, formats, data transformation, verify numeric fields, data cleansing, exception handling, data mapping and some major calculation to be done properly.
Data transformation: By creating a spreadsheet of scenarios and expected results, it generates good requirements elicitation during testing. Creating test data which includes of the scenarios that helps ETL developer to automate the processing of data sets that possess flexibility. Validation of data types in warehouse in the data model. Setting up the data scenarios that tests referential integrity of tables to manage mathematical information.
Data completeness: Data completeness constitutes a record that compares source data, loaded data and rejected data, comparing unique value fields and their distributions in data sets, populating complete contents without truncation in the process and testing all the boundaries to find database flaws or limitations.
Schema level testing: It is a testing methodology that verifies data transformation from source to destination, expected data added in target system, checks DB fields without truncation, verifies checksum for every records, verification of error logs, null values, checks duplicate data and verify data integrity.
Data quality: It is defined as the approach that handles data rejection, substitution and notification without any data changes. To ensure the quality of software, system must reject the record of a particular field which has nonnumeric data. It must validates and correct the state field as per necessity which depends upon the ZIP code and lastly compares the product code to lookup values in a table.
Conclusion:
Collecting and correlating methods in a systematic manner delivers great sort of solutions. Behind the integration of checklist methodology, we have studied and analyse some concepts to portray methods to end with correct destination. Big data testing checklist helps testers to deliver good results by applying a technique in a right way.
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