Systems | Development | Analytics | API | Testing

Improving the security of legacy APIs begins with implementing updated and reliable authentication methods. These measures help block unauthorized access while allowing valid users to interact safely with your APIs.

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

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.

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.

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.

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

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?

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.

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.

Batch testing is the process of executing a group of test cases or scripts together without manual intervention. It is one of the most effective ways to validate multiple features at once, especially during regression testing or nightly builds. Instead of running each test manually, you can automate the batch testing process to save time and reduce repetitive work. This approach is widely used in QA teams to improve test execution speed and catch defects early in the software development lifecycle.