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Sensor Data Analytics. Home Data Analytics 🔎 Big Data 🐘 Big Data Testing. Extensive Guide to Big Data Application Testing ScienceSoft has been rendering all-around software testing and QA services for 34 years and big data services for 10 years.

Table of contents. Big data testing types. Setup plan. Human resources required. Possible sourcing models. Testing costs. About ScienceSoft. Schedule a meeting. Testing Types Relevant for Big Data Applications ScienceSoft's QA specialists conduct the following types of testing in big data projects:.

Performance testing. 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. Check the configuration of role-based access control rules.

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. Based on ScienceSoft's ample experience in rendering software testing and QA services, we list some common high-level stages: 1.

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

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.

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. 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.

Short-list vendors with relevant big data testing experience and technical knowledge. Request a big data app testing presentation and cost estimation from the shortlisted vendors.

Choose the vendor best corresponding to your big data project needs. Sign a big data testing collaboration contract and SLA.

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

Select optimal big data testing frameworks and tools based on ROI analysis. Performance tests measure how a system behaves in various scenarios, including its responsiveness, stability, reliability, speed and resource-usage levels.

By measuring these attributes, development teams can be confident they're deploying reliable code that delivers the intended user experience.

Waterfall development projects usually run performance tests before each release, but many Agile projects find it better to integrate performance tests into their process to they can quickly identify problems.

For example, LoadNinja makes it easy to integrate with Jenkins and build load tests into continuous integration builds. Load tests are the most popular performance test, but there are many tests designed to provide different data and insights.

Load tests apply an ordinary amount of stress to an application to see how it performs. For example, you may load test an ecommerce application using traffic levels that you've seen during Black Friday or other peak holidays.

The goal is to identify any bottlenecks that might arise and address them before new code is deployed. In the DevOps process, load tests are often run alongside functional tests in a continuous integration and deployment pipeline to catch any issues early.

Stress tests are designed to break the application rather than address bottlenecks. It helps you understand its limits by applying unrealistic or unlikely load scenarios.

By deliberately inducing failures, you can analyze the risks involved at various break points and adjust the application to make it break more gracefully at key junctures.

These tests are usually run on a periodic basis rather than within a DevOps pipeline. For example, you may run a stress test after implementing performance improvements.

Spike tests apply a sudden change in load to identify weaknesses within the application and underlying infrastructure. These tests are often extreme increments or decrements rather than a build-up in load. The goal is to see if all aspects of the system, including server and database, can handle sudden bursts in demand.

These tests are usually run prior to big events. For instance, an ecommerce website might run a spike test before Black Friday. Endurance tests, also known as soak tests, keep an application under load over an extended period of time to see how it degrades.

Oftentimes, an application might handle a short-term increase in load, but memory leaks or other issues could degrade performance over time.

The goal is to identify and address these bottlenecks before they reach production. These tests may be run parallel to a continuous integration pipeline, but their lengthy runtimes mean they may not be run as frequently as load tests. Scalability tests measure an application's performance when certain elements are scaled up or down.

For example, an e-commerce application might test what happens when the number of new customer sign-ups increases or how a decrease in new orders could impact resource usage. They might run at the hardware, software or database level. Also known as flood tests, measure how well an application responds to large volumes of data in the database.

In addition to simulating network requests, a database is vastly expanded to see if there's an impact with database queries or accessibility with an increase in network requests.

Basically it tries to uncover difficult-to-spot bottlenecks. These tests are usually run before an application expects to see an increase in database size. For instance, an ecommerce application might run the test before adding new products. If you're testing dynamic data, you may need to capture and store dynamic values from the server to use in later requests.