Relevant Testing Types for Big Data Applications

Functional testing

A big data application comprising operational and analytical parts requires thorough functional testing on the API level. Initially, the functionality of each big data app component should be validated in isolation.

For example, if your big data operational solution has an event-driven architecture, a test engineer sends test input events to each component of the solution (e.g., a data streaming tool, an event library, a stream processing framework, etc.) validating its output and behavior against requirements. Then, end-to-end functional testing should ensure the flawless functioning of the entire application.

Integration testing

For example, if your analytics solution comprises technologies from the Hadoop family (like HDFS, YARN, Map Reduce, and other applicable tools), a test engineer checks the seamless communication between them and their elements (e.g., HDFS Name Node and Data Nodes, YARN Resource Manager and Node Manager).

Performance testing

To ensure the big data application’s stable performance, performance test engineers should:

• Measure the big data app’s actual access latency, the max size of data capacity and processing capacity, response time from different geographical locations (as network throughput may alter through different regions).
• Check the application’s handling of stress load and load spikes.
• Validate the app’s optimal resource consumption.
• Trace the app’s scalability options.

Run tests validating the core big data application functionality under load.

Security testing

To ensure the security of large volumes of highly sensitive data, a security test engineer should:

• Validate the encryption standards of data at rest and in transit.
• Validate data isolation and redundancy parameters.
• Detect the app’s architectural issues undermining data security.
• Check the configuration of role-based access control rules.

At a higher level of security provisioning, cyber security professionals perform an application and network security scanning and penetration testing.

Data warehouse testing

Test engineers check, whether the big data warehouse perceives SQL queries correctly, and validate the business rules and transformation logic within DWH columns and rows.

BI testing, as a part of DWH testing, helps ensure data integrity within an online analytical processing (OLAP) cube and smooth functioning of OLAP operations (e.g., roll-up, drill-down, slicing and dicing, pivot)

Non-relational database testing

Test engineers check how the database handles queries. Besides, it’s advisable to verify database configuration parameters that may demine the application’s performance, and data backup and restore process.

Big data quality assurance

With big data applications, it’s pointless and unfeasible to strive for complete data consistency, accuracy, audit ability, orderliness, and particularly uniqueness (data is replicated by a number of big data app’s components for the sake of fault-tolerance). Still, big data test and data engineers should check if your big data is good enough quality on these potentially problematic levels:

• Batch and stream data ingestion.
• ETL (extract, transform, load) process.
• Data handling within analytics and transactional applications, and analytics middleware.

How to Get Started with Big Data App Testing

The steps of a big data testing setup plan and their sequence may significantly differ depending on your business processes, the big data app’s specific requirements and architecture. Based on our ample experience in rendering software testing and QA services, we list some common high-level stages.

1. Designing the big data application testing process

Assign a QA manager to ensure your big data app’s requirements specification is designed in a testable way. Each big data application requirement should be clear, measurable, and complete; functional requirements can be designed in the form of user stories.

Also, the QA manager designs a KPI suite, including such software testing KPIs as the number of test cases created and run per iteration, the number of defects found per iteration, the number of rejected defects, overall test coverage, defect leakage, and more. Besides, a risk mitigation plan should be created to address possible quality risks in big data application testing.

At this stage, you should outline scenarios and schedules for the communication between the development and testing teams. Thus, test engineers will have a sufficient understanding of the big data app’s schema, which is essential for testing granularity and risk-based testing.

Finally, a QA manager decides on a relevant sourcing model for the big data application testing.

2. Preparing for big data application testing

Preparation for the big data testing process will differ based on the sourcing model you opt for: in-house testing or outsourced testing.

2.1. Preparing for in-house big data application testing

If you opt for in-house big data app testing, your QA manager outlines a big data testing approach, creates a big data application test strategy and plan, estimates required efforts, arranges training for test engineers and recruits additional QA talents.

2.2. Selecting a big data application testing vendor

If you lack in-house QA resources to perform big data application testing, you can turn to outsourcing. To choose a reliable vendor, you should:

• Consider vendors with hands-on experience in both operational and analytical big data application testing.
• Review the vendors’ case studies, pay attention to their big data tech stack.
• Consider if vendors have enough testing resources, as a big data application comprising the operational and the analytical part may require two testing teams of a substantial size.
• Consider vendors’ flexibility and scalability. During a bid data app’s SDLC, the number of testing teams’ members may be altered for the sake of cost-effectiveness.
• Short-list 3-5 vendors with relevant big data testing experience and technical knowledge.

• Request a big data app testing presentation and cost estimation from the shortlisted vendors.
• Consider the vendors’ approaches to big data testing teams’ lineup, the planned test automation involvement, testing toolkit. Choose the vendor best corresponding to your big data project needs.
• Sign a big data testing collaboration contract and SLA.

During big data application testing, your QA manager should regularly measure the outsourced big data testing progress against the outlined KPIs and mitigate possible communication issues.

3. Launching big data application testing

Big data app testing can be launched when a test environment and test data management system is set up.

Big data applications’ size makes it unfeasible to replicate an app completely in the test environment. Still, with the test environment not fully replicating the production mode, make sure it provides high capacity distributed storage to run tests at different scale and granularity levels.

The QA manager should design a robust test data architecture and management system that can be easily handled by all teams’ members, will provide clear test data classification, quick scalability options, and flexible test data structure.

Professional Big Data Testing Services

Big data app testing consulting

• Design a general test strategy and plan for the entire big data application and test strategies for the operational and analytics parts.
• Create a test automation architecture for your analytics and operational big data app’s parts.
• Select optimal big data testing frameworks and tools based on ROI analysis.
• Provide big data testing efforts’ estimation and costs breakdown.
• Select an optimal sourcing model for your big data app testing project.
• Perform root-cause analysis and the mitigation of big data application testing issues, if your big data app testing project is already on the go.

Outsourced big data app testing

• Design the whole big data testing process: an overall QA strategyand test plan for the big data app and more precise ones for analytics and operational parts; test automation architecture with regard to the specifics of each big data app’s part; optimal testing toolkit to cater to the app’s unique tech stack.
• Set up and maintain the test environment, generate and manage test data.
• Develop, execute, and maintain test cases and scripts to ensure each big data application component is optimally designed and configured, seamlessly communicates with others, and all the components comprise one smoothly functioning application.
• Develop a re-usable automated regression test suite for the big data application risk-free evolution.