Automated Big Data Testing Tool

supported database connectors

hadoop
teradata
mongo

TestingWhiz, being automated Big Data testing solution, helps you verify structured and unstructured data sets, schemas, approaches and inherent processes residing at different sources in your application in languages such as ‘Hive’, ‘Map-reduce’ ‘Sqoop’ and ‘Pig’.

With the out-of-the-box connectors - Hadoop, TeraData and NoSQL - of this big data testing tool, you can validate volume, variety and velocity of data, identify the differences and bad data after various implementations, migration and integration processes and ensure functional and non-functional requirements of data are met accurately to perform error-free processes and analytics.

capabilities

Post ETL Data Validation
Data Migration Validation
Big Data Health Check

Post ETL Data Validation

TestingWhiz provides big data test automation solution to validate whether the data accumulated and loaded after the ETL process is assorted, robust and capacious to drill important insights.

Analysis of large data sets post ETL
Validation of data extraction, transformation and loading into EDW
Validation of results

Data Migration Validation

TestingWhiz's Hadoop test automation helps automate the process of connecting and extracting data from large clusters of Hadoop, performing data migrations between RDBMS and Hadoop-based data sets and comparing data sets between HDFS and RDBMS.

Source to Target (NoSQL DB/ Hive/ HDFS) Validation
Post migration data accuracy validation
Validation of Hadoop process data output
Validation of reports

Big Data Health Check

TestingWhiz helps automate checking of your Big Data pools by verifying their quality, integrity and scalability to authenticate and ensure their adherence to applications for further usage and analysis.

Performance testing of Big Data architecture and volume
Hive queries & Pig jobs validation
HDFS & NoSQL database validation

How it works

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benefits

Real-time information due to integration of large data sets across multiple sources
Better data quality enabling correct decisions and subsequent actions
Better alignment of data with changing dynamics to take predictive actions
Right insights from the minutest data sources
Scalable data across different layers and touch-points