Data Testing

Services

Shaping the Future of Business with Intelligent AI Services

TESTING SERVICES FOR NEXT GENERATION NEXUS
We ensure Data Quality and Accuracy with BlisMos Next-Gen Nexus, utilizing cutting-edge technologies.

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ETL Testing

ETL Extract Transform Load is a process that extracts data from source systems, transforms the information based on a set of business rules, then loads the data into a single repository. ETL testing refers to the process of validating, verifying and qualifying data while preventing duplicate records and data loss.

Data Validation

Data Validation ensures the accuracy and quality of the data in different storage systems.

Process Validation

Process validation refers to validating the data as they pass various stages of the system. This generally involves business logic validation, data aggregation, and segregation, and checks key-value pair generation in different parallel processing models like Apache Spark.

Output Validation

Output validation involves the elimination of data corruption, successful data loading, maintenance of data integrity, and comparing the storage data with target data.

Stages of the ETL testing process

Effective ETL testing detects problems with the source data early on – before it is loaded to the data repository — as well as inconsistencies or ambiguities in business rules intended to guide data transformation and integration.

Validate Data Sources

Perform a data count check and verify that the table and column data type meet the specifications of the data model. Make sure check keys are in place and remove duplicate data. If not done correctly, the aggregate report could be inaccurate or misleading.

Validate Transformation logic

Ensure whether the transformation is in line with the business rules applied to the source data or not. Check data threshold, alignment, direct moves, defaulting, filtering and look-ups match the mapping document for each column and table.

Load validation

Perform a record count check before and after data is moved from staging to the data warehouse. Confirm that:

Invalid data is rejected and the default values are accepted.
Data sets in staging to Loading area works as expected
Historical loading works as expected
Incremental and total refresh work as expected.

Big Data Testing

The ultimate use of Big Data is its ability to give us actionable insights. Inferior quality data leads to poor analysis and hence to poor decisions. Errors in data of pharmaceutical companies or banks or investment firms may lead to legal complications. Ensuring accuracy of data leads to correct human engagement and interaction with the data system.

We ensure the quality of the data by rigorous and detailed end to end testing across the different stacks of the Big Data ecosystem. As a team of Big Data SDETs(Software Development Engineers in Testing), we understand the nitty-gritty of Big Data development and fundamentals of testing methodologies.