Systems | Development | Analytics | API | Testing

Automation testing has become an essential part of modern software delivery. It helps teams release faster while ensuring quality at every stage of development. A well-defined automation test strategy gives QA teams a roadmap to plan, build, and execute automated tests effectively. Behavior-driven development (BDD) takes this further. It bridges the gap between business stakeholders, developers, and testers by using plain language scenarios.

Manual web testing is still the backbone of quality assurance for many teams. It gives you control, flexibility, and insight that automated tests can’t always match. Before automation tools are introduced, or even alongside them, manual QA testing have helped QA teams identify visual bugs, navigation issues, broken flows, and inconsistencies in real-world user behavior. They are a little bit tedious, but they sure are helpful. This guide will walk you through.

No one starts their work day thinking “Let’s investigate the memory allocations of my Gradle build”, but sometimes life happens (usually at the worst possible time): Every Gradle project faces memory allocation problems eventually, as the codebase grows. So, instead of blindly applying JVM flags from Stack Overflow until it’s resolved, why not take a deeper look?

BigQuery short query optimizations accelerate small queries by executing them as a single stage. It can significantly improve slot time and latency.

Stories are how humans make sense of complexity. We remember cause and effect, not pie charts. We respond to tension, not tooltips. And yet, in most businesses, “data storytelling” still means downloading a CSV, sending screenshots in Slack, or fumbling through a slide deck minutes before the meeting. The promise was beautiful. Democratize data. Make every decision backed by fact, not gut. Put analytics in the hands of the many, not the few.

In the evolving landscape of AI applications, the Model Context Protocol (MCP) emerges as a pivotal standard, facilitating seamless integration between large language models (LLMs) and external tools, data sources, and services. By standardizing these interactions, MCP enables AI systems to perform complex tasks with enhanced context and precision. To harness the full potential of MCP, developers require robust tools that ensure reliability, scalability, and efficiency.

Manufacturers are drowning in data but struggling to use it effectively. Sensors on factory floors generate massive amounts of information - temperature, vibration, pressure - but much of it sits in isolated systems, creating "data silos." These silos prevent real-time decisions, predictive maintenance, and cost savings. The solution? Data AI Gateways. These gateways unify isolated sensors, process data locally with edge computing, and translate protocols to connect legacy equipment with modern systems.

Turn your warehouse into a growth engine — sync trusted data directly into Salesforce, Marketo, Zendesk, and more.

APIs have become the backbone of communication between applications in today’s software-oriented world. Digital systems are increasingly complex due to the proliferation of microservices and distributed systems, making API testing both efficient and crucial. A recent study projects the API testing industry to grow from USD 4.92 billion in 2025 to USD 27.38 billion by 2034, reflecting a compound annual growth rate (CAGR) of 20.99% during the 2025-2034 timeline. Source: Market Research Future.

Common Vulnerability and Exposures (CVE) collects known cybersecurity vulnerabilities and exposures to help you to better safeguard your embedded software. This framework is central to managing security threats effectively. Here, we explain what is CVE, unpack the role of CVE identifiers, examine the differences of CVE vs. CWE, expand on the CVE list, and outline how identifying vulnerabilities early in software development can be achieved with the right static analysis tools.