Systems | Development | Analytics | API | Testing

The transformation from “testing” to “Quality Assurance (QA)” and then to “Quality Engineering (QE)” has been quite impressive, and it likely has a few more surprises in stock. In our previous article of the "Top 5 software testing trends of 2024", we highlighted the overall “quality at speed” theme and several key trends such as DevOps and Automation. But what exactly do those trends mean for you as the buyer and user of QE tools?

Ever found yourself frustrated by glitches while shopping online? Picture this, you are about to make that crucial purchase, but suddenly, an error message pops up, stopping your transaction. What's behind these digital glitches, and how can they be fixed? That is where Golang logging steps in! When you encounter an error during checkout, Golang logging jumps into action, capturing crucial details about what went wrong.

Over the past several years, many organizations have shifted their technology focus to the cloud. From a technical perspective, that has required a delicate balancing act, managing tradeoffs between the old and the new. Business leaders have sought to minimize the level of disruption their teams face while modernizing business applications, increasing integration, and adding important new functionality.

While our discussions have previously highlighted the unique analytics challenges and opportunities within industries such as automotive, healthcare, and kids' gaming, today, we turn our focus to the finance sector. In this article, we’ll zoom in on analytics challenges in the finance world, and we’ll answer why Countly is exceptionally suited for product analytics in the financial sector.

So, you’ve built a cutting-edge AI model! Congratulations! With the vast array of AI capabilities coming to market, it may generate realistic product descriptions, analyze customer sentiment, or perform cutting-edge processing. Innovation with AI is helping companies to discover new capabilities and use cases. However, innovation alone isn’t enough. To truly reap the rewards of an investment in AI, companies need a strategy to monetize it.

Combining the impressive cloud storage capabilities of Snowflake with the robust and versatile processing tools of Microsoft Excel is the type of data wizardry that can deliver incredible results. By leveraging these two powerhouses, developers can tap into unparalleled data analysis, visualization, and reporting, transforming raw data into insightful decisions with ease.

Rate limiting is a fundamental mechanism for controlling the number of requests a client can make to a server in a given time frame. In a world where more than 30% of web traffic comes from malicious bots, that proactive strategy is critical to protect servers from abuse. In this tutorial, you'll delve into the concept of rate limiting and understand why you need it in your Node.js backend. Then, you'll learn how to implement it in Express to block or slow down incoming excess requests.

If you’ve tried building real-world features with AI, chances are that you’ve experienced reliability issues. It’s common knowledge that AI makes for great demos, but… questionable products. After getting uncannily correct answers at first, you get burned on reliability with some wild output and decide you can’t make anything useful out of that. Well, I’m here to tell you that there’s hope.

The digital landscape has witnessed an explosion of APIs in recent years. These powerful tools act as the glue binding applications together, facilitating data exchange and powering our interconnected world. But with this rapid rise comes a growing concern: securing these APIs to safeguard sensitive information. In our recent webinar at SmartBear, "Defend Your APIs: Secure by Design," API experts Frank Kilcommins and Yousaf Nabi unpacked some critical strategies for keeping your APIs safe.

A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. The goal is to ensure that organizational data meets specific standards, i.e., it is accurate, complete, consistent, relevant, and reliable at all times—from acquisition and storage to subsequent analysis and interpretation. eBook: A Guide to Data Quality Management Download eBook.