Data Testing

ETL Testing

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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.
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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.
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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.
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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

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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.

Data Validation

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

Process Validation

Process validation 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.
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Big Data Ingestion

Big Data ingestion is a process of connecting to disparate sources, extracting the data, and moving the data into Big Data stores for storage and further analysis. We are experts in prioritizing data sources, validating individual files, and routing data items to the correct destination. We choose the most appropriate tool specific to a project out of multiple options available from open source data ingestion tools or tools provided by cloud solutions providers or building a data ingestion tool.
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Big Data Storage

During business Big Data storage is concerned with storing and managing data in a scalable way, satisfying the needs of applications that require access to the data. The ideal Big Data storage system would allow the storage of an unlimited amount of data, cope both with high rates of random write, and read access. The storage system flexibly and efficiently deals with a range of different data models, supporting both structured and unstructured data. There are challenges like Volume, Velocity, Variety in storing Big data. We address these challenges by making use of distributed, shared architectures. Choosing an optimal storage system allows addressing increased storage requirements by scaling out to new nodes providing computational power and storage.
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Big Data Processing

Big Data processing encompasses a set of techniques used before the application of a data mining method as large amounts of data are likely to be imperfect, containing inconsistencies and redundancies and not directly applicable for starting a data mining process. Big Data processing includes a wide range of disciplines, data preparation, data reduction techniques, data transformation, integration, cleansing, and normalization. We have vast experience in applying the optimal techniques by choosing the appropriate tools to process the data as per the client project needs. After the application of a successful Big Data preprocessing technique implemented by our team, the final data set obtained is reliable and suitable for any further processing or downstream applications.
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Big Data Visualization

Big Data visualization refers to the implementation of more contemporary visualization techniques to illustrate the relationships within data. Visualization tactics include applications that can display real-time changes and more illustrative graphics, thus going beyond pie, bar, and other charts. We can seamlessly integrate the suitable visualization tools with the processing tools to depict the insights or patterns.
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BLISMOS SOLUTIONS PRIVATE LIMITED
# 83, 3rd main, Indira Gandhi Main Rd,
A Narayanapura, Bengaluru, Karnataka 560016
info@blismos.com
+91 9972257724
www.blismos.com

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