Systems | Development | Analytics | API | Testing

Using Webhooks to Integrate Confluent Cloud and Microsoft Teams

Data streaming equips modern organizations to rapidly ingest and understand new information and use it to solve real-world problems at scale. For some of these real-time insights—critical operational cues that demand a timely response—delivering that information directly to your team’s inbox is the best way to act on it.

Real-Time AI at Scale: The New Demands on Enterprise Data Infrastructure

Real-time AI is transforming how businesses process and use data, demanding faster, more reliable, and scalable infrastructure. Unlike older batch processing systems, real-time AI provides instant insights for applications like fraud detection, personalized recommendations, supply chain adjustments, and predictive maintenance. However, scaling these systems introduces challenges like managing massive data streams, ensuring low latency, and maintaining security.

Android Studio Tutorial: Build and Publish Your First App

Android app development is the process of building software for Android devices, such as smartphones, smart TVs, tablets and wearables. It’s a Linux-based system and open source, which means manufacturers can customize it. Android version names used to be named after desserts, like Lollipop, Marshmallow, KitKat and Oreo. From version 10, Google switched to number-based names like Android 10, Android 11, Android 12, etc, up to the most recent, Android 15, which was released in September 2024.

How Iceberg Powers Data and AI Applications at Apple, Netflix, LinkedIn, and Other Leading Companies

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

Implementing API Governance Policies in WSO2 API Manager

Growing business demands require enterprises to manage a vast number of APIs. The volume and complexity of these integrations make maintaining quality a challenge. To address this, governing the API lifecycle—from design to retirement—has become essential. API management vendors are therefore developing tools and frameworks to assist.

Monitor API Calls In Chrome And Validate Flask Apis

You have probably seen pages where fresh information loads in without a page reload, or some forms that submit without an apparent refresh. What is happening here is that API calls are being made to send and receive information in background. API calls generate seamless and responsive application experiences. In this introductory tutorial, you will learn to examine an API call in the Chrome DevTools; replay it in Python, and sanitise the Flask REST API data for safe and organised input into the database.

What Is API Testing? Exploring Core Of Reliable Software

In an increasingly connected digital ecosystem, with applications allowing for smooth communication across platforms and services, it is important to understand what is API testing in order to maintain the delivery of reliable software solutions. API testing is foundational to modern software assurance, effectively auditing the invisible bridges lending themselves to data us and functionality between different software components to ensure they are working optimally numerous conditions and scenarios.

20 End-to-End Test Management Software for 2025

Choosing the right tool for quality assurance is not easy. There are so many options that promise to handle everything from planning to reporting. That is why we put together this guide to the 20 end-to-end test management software for 2025. These tools are built to manage the full testing lifecycle in one place, from test case creation to execution, analytics, and reporting.

What Can Go Wrong? Understanding Risk & Failure Modes in Agentic AI

Agentic AI systems don’t fail like traditional software - they hallucinate facts, pursue the wrong goals, overuse tools, and forget context. These failures look “correct” to traditional test cases, but feel dangerously wrong to users. One team tested an AI support bot - it passed every check, but in production, it gave refund advice that violated company policy. Not a code error. A reasoning failure.

Lower Cloud Bills, Faster MTTR, Stronger Security: One Platform for Node.js

Performance and efficiency aren’t just technical concerns, they’re business-critical. For companies running Node.js applications, hidden inefficiencies can quietly drive up costs, slow down innovation, and increase risk. N|Solid transforms the way businesses manage and optimize their Node.js applications.