Systems | Development | Analytics | API | Testing

APIs have become the backbone of our digital world, with surveys showing that over 70% of developers plan to increase API usage year-over-year. They power everything from mobile apps and SaaS integrations to IoT devices and partner platforms, enabling businesses to deliver seamless services and experiences to customers. As organizations grow, however, so does the complexity of their API ecosystem.

If you’re feeling intimidated about creating your first automated web test with Ghost Inspector, we’ve got you covered! In this article, we show you how easy it is to build and automate web tests with Ghost Inspector. We’ll break down creating your first Ghost Inspector test in just four simple steps. Once you’ve learned the basic steps for how to build a web test, you’ll be able to test complex processes easily, even with zero coding know-how. Ready? Let’s jump in.

Did you know that the average code coverage across industries falls between 74% to 76%?

Join Danielle Forier, Software Quality Assurance Analyst, as she shares the journey of how her QA team transformed their testing strategy by integrating functional and performance testing. Discover how reusable scripts and the right tools helped them achieve seamless workflows, greater efficiency, and the scalability needed to manage a growing and complex product portfolio.

Many organizations run Apache Kafka clusters in private Azure networks to meet stringent security, compliance, and operational requirements. However, securely replicating data across clusters without exposing traffic to the public internet has traditionally been complex, requiring self-managed mirroring solutions with significant operational overhead.

LLM Data Gateways are specialized tools that prepare and secure data for AI systems, ensuring better performance, compliance, and cost efficiency. They act as a bridge between raw data and large language models (LLMs), solving common challenges in AI like poor data quality and security risks.

Ruby and Rails are great tools that allow you to create complex web applications quickly. Well, some kinds of complex web applications. While they excel at traditional, monolithic, server-rendered applications, they fail to excel at delivering real-time or distributed services. This is why it's so handy for Rubyists to learn a programming language like Go. Go is designed to write lightweight services that handle lots of inbound connections.

Measuring DevOps success? You need to use DORA metrics from the DevOps Research and Assessment (DORA) framework. These metrics help software teams get better over time. Think of DORA metrics like the stats used in Formula One racing. In Formula One, teams collect and analyze thousands of metrics before and during each race to optimize car setups, detect potential failures early, and refine strategies for peak performance.

Artificial Intelligence (AI) is transforming the way businesses manage, process, and analyze data. AI Data Management involves the use of machine learning (ML), automation, and intelligent data pipelines to enhance data storage, governance, integration, and security. As organizations deal with ever-growing datasets, AI-driven data management solutions ensure efficiency, scalability, and accuracy.

In the modern data-driven landscape, enterprises require robust platforms to handle massive volumes of structured and unstructured data efficiently. Databricks, a unified analytics platform built on Apache Spark, has emerged as a leader in data management, offering high-performance computing, collaborative workspaces, and seamless integrations with cloud providers. In this blog, we explore how Databricks transforms data management, its core components, and best practices for optimizing data pipelines.