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Enterprise Java applications do not often break due to business logic. The reason they break is that dependency ecosystems evolve all the time. Manual maintenance in most large systems consists of hundreds of third-party libraries, and small upgrades occur regularly as a result of security patches, code corrections, or vendor advice. The problem is not recognizing outdated libraries. Tools such as OWASP Dependency-Check, Snyk, and Black Duck already do it well.

The problem is a wastage of the developer's time in repetitive actions: checking Maven Central for the latest versions, validating whether the upgrade is safe, reading release notes, guessing what test cases should be executed, and raising a pull request with meaningful documentation.

APIs are at the heart of almost every application, and even small issues can have a big impact. Data-driven API testing with JSON files using REST Assured and TestNG makes it easier to validate multiple scenarios without rewriting the same tests again and again. By separating test logic from test data, we can build cleaner, flexible, and more scalable automation suites.

In this article, we’ll walk through a practical, beginner-friendly approach to writing API automation tests with REST Assured and TestNG using JSON files as the data provider.

Data-driven testing enables testers to execute the same test logic with multiple sets of input data, improving coverage and reliability with minimal effort. By combining CSV files with TestNG’s @DataProvider annotation, test data can be easily separated from the test logic. This approach enables maintainability and makes test automation more scalable and flexible.

This article explains how to implement data-driven testing with CSV files and TestNG in a clear, practical, and easy-to-follow manner.

In the previous article, we explored how to implement data-driven testing using Object arrays and TestNG’s @DataProvider annotation. While this approach works well for small to medium-sized datasets, it is not ideal for handling large volumes of data. To address this limitation, TestNG also supports the use of Iterators, which provide a more efficient way to manage large and dynamic datasets.

This article focuses on how to perform data-driven API automation testing using an Iterator with a DataProvider annotation of TestNG.

Building enterprise Java systems used to mean choosing an app server, deploying a monolith, and scaling vertically until the budget or the database complained. In 2026, modern Java teams are expected to deliver faster releases, better resilience, and elastic cost-performance across unpredictable workloads. That’s exactly what cloud-native Java architecture is designed to achieve: systems built for change, not just for uptime. 

But “cloud-native” is not a buzzword synonym for “running on Kubernetes.” A truly scalable approach combines Java microservices (for domain isolation and independent delivery) with Serverless Java (for bursty or event-driven workloads), backed by Kubernetes for Java as the operational substrate for consistent deployment, resilience, and observability.