“Assurance of Quality, Performance, and Reliability”


Big Data is a vast collection of data, both structured and unstructured. Businesses have the chance to use these massive amounts of data to make informed decisions by using the right analytics.

Analytics is the act of finding, analyzing, and communicating significant patterns in large amounts of data as well as the process of using such patterns to make wise decisions. Our Big Data & Analytics Testing services provide validation of the processing of both structured and unstructured data, migration & transformation using Data Warehouse tools, and testing of different models across platforms. Our end-to-end testing strategy ensures that the system requirements and test data requirements are tested, as well as periodic vulnerability assessments and system health checks by benchmarking key performance metrics.



Functional Testing

In-depth functional testing at the API level is necessary for big data applications that include both operational and analytical components. The functionality of each part of a big data app should first be independently verified.

Integration Testing

This sort of testing verifies the smooth interaction of the complete big data application with external applications, within and between various big data application components, and between various technologies. It is done taking into account the distinct architecture and tech stack of your application.

Data Wharehouse Testing

Test engineers validate the business rules and transformation logic contained within DWH columns and rows, as well as whether the massive data warehouse correctly interprets SQL queries. Online analytical processing (OLAP) cube data integrity and efficient OLAP operations are ensured by BI testing, which is a component of DWH testing (e.g., roll-up, drill-down, slicing and dicing, pivot).

Big Data Quality Assurance

While data is replicated by a number of big data app components for the sake of fault-tolerance, it is meaningless and impractical to aim for total data consistency, accuracy, suitability, orderliness, and notably uniqueness. Therefore, big data testing and data engineers should determine whether your big data is of sufficient quality on these potentially hazardous levels.

Non-Relational Database Testing

Testers examine the database's query handling capabilities. Also, it's wise to double-check data backup and restoration procedures as well as database configuration elements that can impair the functionality of the programme.

Performance Testing

Performance test engineers should: to guarantee the reliable performance of the big data application. To Check the big data app's actual access latency, maximum data, and processing capacity, and response time from various locations. Analyze how the application responds to stress and load surges. Verify the app's recommended use of resources.

Lotus BigData Testing Solutions Advantages

  • Automate functional testing of web, mobile, Cloud, Windows, and Java applications.
  • Our framework Supports the needs of both beginner and experienced testers.
  • Quickly generate quality metrics reports for management
  • Lotus ALM solutions help your organization more effectively automate tests to improve quality.
  • Experience designing and implementing test frameworks (Modular, Data-driven, Keyword-driven, Object Repository, and Descriptive, among others).
  • Technology expertise for implementing custom automation harnesses and utilities in different languages such as C#, .Net Java, Perl, and Python, among others.
  • We partner with our clients’ innovation teams using our in-depth understanding of the constantly evolving digital landscape, consumer behavior, and both established and emerging technology.


What our clients say about us

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    Aaron Moore
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Our Clients

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We can discuss your IT needs and challenges, and our solutions architects can schedule a free 30-minute call.