Technology, changing at a breakneck speed, has never raised higher demands for practitioners who can guarantee the integrity, security, and performance of large-scale applications. Viharika is at the forefront of this transformation, with more than a decade of experience as a Senior QA Engineer and Analyst. This includes big data, cloud environments, performance testing, and automation, which make her one of the most valuable assets in all the projects she undertakes. This interview outlines the not-so-well-known aspects of her career and reveals the practices that have made her a game-changer in the field of technology.
1. You have worked with Big Data technologies such as Hadoop, Spark, and HBase. How do you go about testing in these large-scale environments?
Big data testing requires a pretty different paradigm because volumes are huge and data processing needs to be in real time. The emphasis is on creating test strategies that will efficiently validate processes for data ingestion, data transformation, and data extraction. Ensuring data integrity during an ETL is one of the biggest challenges. Tools like JMeter, Shell, Perl and Python scripts help in emulating big volumes of data to be processed and, at the same time, allow validation at various steps. Moreover, when working on distributed systems, like Hadoop and Spark, partitioning the data and processing over nodes becomes very crucial to ensure performance and scalability.
2. How can one ensure correctness and performance of data pipelines in cloud environments such as AWS?
Cloud environments bring their own different problems, especially in the context of data pipelines. I have performed extensive work within AWS on services like EMR, Lambda, and DynamoDB, where high accuracy and performance regarding the pipelines matter a lot. I focus on real-time monitoring by using such tools as CloudWatch and Splunk for tracking the health of data pipelines to catch issues early. Automation also plays a major role here. I have written Python scripts that trigger the events, test the data flow, and run various scenarios to ensure everything in the pipeline flows correctly. The most rewarding experience that comes to my mind is using AWS Glue for automating data crawling to transform raw data into insightful information by keeping a constant eye on potential performance bottlenecks.
3. You have been involved in the performance testing of large-scale applications. How would you handle JVM performance tuning and Heap Dump Analysis?
In large-scale JVM-based applications, performance tuning should be done to sustain peak loads. My approach will be to work on heap size tuning and garbage collection mechanisms so that JVM can sustain the load without running out of memory. I do this through real-time performance monitoring using tools like JMeter and AppDynamics, thread dump analysis, and heap dump analysis. If the performance degradation is observed, deep diving into heap dump analysis will be useful to identify memory leaks or objects holding onto space for no reason. Because a well-tuned JVM ensures that applications will be scalable, not only at this moment but also sustaining performance over a long period of time under continuous use.
4. You mentioned microservices architecture. How do you ensure the integration and performance of microservices in a large ecosystem?
Microservices are all about decoupling functionalities for scalability and flexibility, but challenges arise in keeping all services together smoothly. My approach is all about integration testing and validation of performance. I manage the deployment of microservices using tools like Kubernetes/Docker and deploy the services, and I test the communications between services using tools like JMeter under a variety of loads. Monitoring is paramount-with tools like Grafana, I create dashboards tracking performance for each microservice that would highlight potential bottlenecks. That included processing microservices for advertisements using Grafana and Splunk to spot latency issues quickly and find their solutions to maintain the user experience easily.
5. What role does AWS Lambda play in your projects, and how do you optimize its performance for critical tasks?
AWS Lambda has been a game-changer to automate serverless functions, mainly real-time data processing. I perform a number of optimizations for Lambda functions-starting with their consumption of memory and execution time. One such function was being used in our project for data aggregation in ad processing. Tuning it reduced execution time significantly. The functions also carry out CloudWatch logs for Lambda executions, alarm setup to keep a tab on any anomaly, and continuous function optimizations to improve performance. Integration with other AWS services, such as DynamoDB and Kinesis, makes Lambda an extremely efficient service for highly critical and time-sensitive tasks.
6. You have worked with a number of test environments ranging from on-premise environments to cloud-based environments. How do you cope with changing from one environment to another?
Each of these kinds of environments will have its own set of requirements and its own list of challenges. On-premise environments themselves tend to be more about infrastructure management and making sure the servers can sustain the workload. It's more about effectively managing the resources in cloud environments and leveraging scalability through platforms like AWS and Azure. In terms of application migrations from one environment to another, compatibility testing of both environments is essential. I run a lot of end-to-end tests in both environments and use automation scripts to make sure things work as expected. One of my cloud migration projects was to move a legacy system into AWS, and testing was a critical task to ensure the performance metrics were similar in both platforms.
7. Automation is one of your core strengths. How do you go about selecting the automation tool or framework to adopt for a given project?
Automation tooling and frameworks depend basically on project requirements and technologies involved. With the integration of AI tools like Microsoft Copilot and GitHub Copilot, I've been able to enhance my automation strategies significantly. For web-based applications, I use Selenium or Cypress, while for APIs, my go-to tools are Postman or REST-assured. In terms of performance testing, I have used JMeter for more complex applications with heavy traffic. The exciting part is how AI tools can now help optimize these test scripts and identify potential issues before they arise. It's all about choosing the right combination of traditional automation tools and AI-powered solutions, then seamlessly integrating them into your CI/CD pipeline. When selecting tools, I look at community support, scalability, and integration capabilities with AI platforms. Tools such as Jenkins or Appium, especially when enhanced with AI capabilities, are super scalable and very well supported, making them ideal for projects that develop over time.
8. How do you keep yourself updated with the latest trends and technologies concerning quality assurance and testing?
Keeping current with the rapidly evolving technology landscape is crucial. My Azure AI Engineer Associate certification and AWS Fundamentals of ML and AI certification have been instrumental in understanding how AI can revolutionize testing processes. I actively invest time in web courses, conferences, and webinars to learn new tools and methodologies, particularly in the AI and ML space. Platforms like LinkedIn and GitHub are great resources to stay connected with the QA community and exchange ideas about emerging AI technologies in testing. My fellowship memberships in IEEE, IET, and BCS provide access to cutting-edge research and developments in AI-powered testing solutions. Furthermore, getting hands-on experience with new tools, especially AI-powered ones like Microsoft Copilot and GitHub Copilot, is one of the best ways to stay sharp. I regularly experiment with new testing frameworks and automation tools, incorporating AI capabilities whenever possible. This helps me learn something new and keeps me at the edge, enabling me to innovate solutions that combine traditional testing approaches with modern AI capabilities.
9. How would you ensure security in testing, especially with sensitive data like user information and financial data?
Security is of utmost importance, especially when sensitive information such as user credentials or financial information is at stake. First of all, I take care about test environments, keeping them properly secured by using at least encryption protocols and access restrictions. The CI/CD pipeline includes designed automated security tests to find out and fix vulnerabilities as early as possible. For cloud platforms projects, one has used AWS security services: IAM and CloudWatch to monitor and control access. One important area of concern is ensuring that sensitive information is anonymized in test environments. This is done with the use of data masking techniques and ensuring test integrity in the process.
10. How do you balance speed in software delivery with maintaining high standards of quality in testing?
With fast paced development cycles today, it is very easy to take shortcuts by giving speed more prominence over quality. On the other hand, I find automation to be the key that balances it. Automation of repetitive tasks like regression testing and performance benchmarking frees my time to devote more effort in high-risk areas that require complex testing and cannot be automated. Continuous integration with tools like Jenkins can be used to deploy code quickly by running automated tests in the background. It is also very important to communicate with the stakeholders so that realistic expectations can be set on what can be achieved within certain times of the timeline, which helps ensure that quality never suffers just to meet the deadlines.
Conclusion:
Viharika's expertise in Big Data, cloud environments, and automation, coupled with her advanced AI certifications and practical experience with AI tools, has positioned her as a pioneer in quality assurance. Her Microsoft Azure AI Engineer Associate certification and AWS Fundamentals of ML and AI certification reflect her commitment to staying at the forefront of technological advancement. With great skill in navigating complex systems and leveraging AI-powered tools like Microsoft Copilot and GitHub Copilot, she consistently pushes the boundaries of what's possible in testing. Her story of constant innovation in the tech industry, particularly in integrating AI into testing processes, is a source of inspiration to aspirants in the QA profession. It serves as a reminder that true breakthroughs come from the intersection of traditional testing expertise, modern technology, and artificial intelligence.
