Quality Thought Training Institute

  In today’s microservices-based architecture, managing and securing communication between services becomes a significant challenge. As the number of services grows, so do the complexity and the need for traffic control, observability, and security. Service mesh tools like Istio come to the rescue by offering a dedicated infrastructure layer to manage service-to-service communication in a transparent and efficient manner. This blog will explore how to implement a service mesh using Istio in a Flask microservices setup. What is a Service Mesh? A service mesh is an infrastructure layer that controls and observes communication between microservices. It typically uses lightweight network proxies deployed alongside services (called sidecars) to intercept and manage all incoming and outgoing traffic. Istio is one of the most widely used open-source service mesh tools. It provides: Traffic management Observability and telemetry Security with mutual TLS Resiliency (retries, failovers, etc....

In the insurance industry, efficient billing and payment processing are critical for maintaining customer satisfaction and operational accuracy. Guidewire BillingCenter, a part of the Guidewire InsuranceSuite, is a powerful platform that automates and manages billing processes across the policy lifecycle. One of its core strengths lies in handling premium payments and refunds—ensuring timely transactions, regulatory compliance, and a seamless user experience. In this blog, we’ll explore how Guidewire BillingCenter manages premium payments and refunds effectively. What is Guidewire BillingCenter? Guidewire BillingCenter is an enterprise-grade billing management system designed for property and casualty (P&C) insurers. It provides end-to-end capabilities to manage customer accounts, billing schedules, payments, invoices, dunning, write-offs, and refunds. It integrates seamlessly with other Guidewire products like PolicyCenter and ClaimCenter, creating a unified insurance operations e...

As data volumes continue to grow, choosing the right file format for storing and processing big data is critical for performance, scalability, and cost efficiency. Among the most popular choices are Avro, Parquet, and ORC—each optimized for different use cases. While they all support schema evolution and work well with modern data tools like Apache Spark, Hive, and Hadoop, they have key differences in structure, performance, and compression. This blog compares Avro, Parquet, and ORC to help you decide which columnar file format suits your data needs best. 1. What is a Columnar File Format? In columnar storage, data is stored column by column rather than row by row. This approach drastically improves read performance for analytical queries that only access specific columns, making it ideal for OLAP (Online Analytical Processing) and big data analytics. 2. Overview of Each Format Developed by: Apache Storage format: Row-based Serialization: Compact binary format with embedded schema Best...

As organizations invest in test automation tools like Tricentis Tosca, one of the key concerns from stakeholders is understanding the return on investment (ROI). Measuring ROI in test automation helps justify the cost, optimize resource allocation, and guide future strategies. However, calculating ROI isn’t just about comparing costs—it involves assessing efficiency gains, risk reduction, and long-term benefits. In this blog, we’ll explore how to measure ROI from Tosca automation and what metrics matter most. Why Measure ROI in Test Automation? Manual testing is time-consuming, error-prone, and expensive at scale. Tosca, a model-based automation tool, addresses these issues by: Reducing test cycle time Increasing coverage Enhancing reusability Supporting continuous testing However, to validate its impact, you need a clear, data-driven ROI analysis. Key Components of Tosca Automation ROI 1. Initial Investment This includes all upfront costs such as: Licensing fees for Tosca Infrastructu...

When automating web applications with Selenium Python, controlling the browser window size is essential for testing how your application behaves in different screen resolutions and layouts. Whether you're validating responsive designs, working with headless browsers, or ensuring consistency across test environments, setting the browser window size gives you precise control over the visual and functional aspects of your automated tests. In this blog, we’ll explore different ways to set and manage browser window size in Selenium with Python. Why Set Browser Window Size? Different users access web applications from devices of various sizes—desktops, tablets, and mobile phones. Therefore, it’s critical to: Test responsive design across breakpoints (e.g., 1920x1080, 1366x768, 768x1024). Capture consistent screenshots during test runs. Ensure visibility of UI elements that may appear/disappear at different resolutions. Avoid test flakiness due to hidden or off-screen elements. By program...

One of the most common exceptions encountered while automating web applications using Selenium is the StaleElementReferenceException. It often frustrates testers by appearing inconsistently and breaking test execution. Understanding what causes this exception and how to handle it effectively is essential for writing stable and reliable Selenium Java tests. In this blog, we’ll explore the causes of StaleElementReferenceException and discuss multiple strategies to handle it. What is a StaleElementReferenceException? A StaleElementReferenceException in Selenium occurs when a WebElement that was previously located becomes invalid or outdated, typically because the DOM has changed since it was first found. Example Scenario: You locate a button and store it in a variable: java WebElement myButton = driver.findElement(By.id("submit")); Then, the page is refreshed or an AJAX call updates that part of the DOM. When you try to interact with myButton: java myButton.click();  // This thr...