Systems | Development | Analytics | API | Testing

The architecture of the modern data lake offers considerable advantages over data warehouses.

As your user base expands, so does the volume and variety of push notifications you need to send. Whether it’s transactional alerts, updates, or personalized messages, publishing notifications for thousands - or even millions - of users can quickly become a bottleneck. The more you grow, the more important it becomes to have a scalable, efficient push strategy.

Regular expressions are usually not top of mind when you think of performance bottlenecks in your application, but inefficient patterns or sub-optimal use of the APIs can make them slow. A wonderful learning opportunity for this is a recent roughly factor of 2x performance improvement patch to Doctrine DBAL’s SQL Parser. This patch was submitted by Soner from Shopware fame, thank you to him.

Are you leading the development or marketing of an app or website that involves protected health information (PHI)? If you have not heard the term before, PHI involves information related to an individual’s health status, healthcare, and healthcare payments. The Health Insurance Portability and Accountability Act (HIPAA) protects said individuals and their health-related information, which you must comply with before progressing further with your product or service.

Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

The retail industry has been shaped and fundamentally transformed by disruptive technologies in the past decade. From AI assisted customer service experiences to advanced robotics in operations, retailers are pursuing new technologies to address margin strains and rising customer expectations.

When evaluating a DevOps testing service, software teams should ask the following questions — it can mean the difference between quickly shipping high-quality code and bottlenecking releases, or having bugs slip into production: In this article, we review five top DevOps testing services, starting with our own, Rainforest QA. We show how we help teams ship high-quality code without constant bottlenecks dragging down the release pipeline.

Today, we are excited to announce our partnership with Pruna AI. Pruna AI is the optimization engine built to simplify and accelerate scalable inference. Koyeb offers a serverless cloud platform for teams to deploy ML and AI models on high-performance GPUs, CPUs, and accelerators - globally. By combining Pruna with Koyeb, you can speed up your model optimizations, achieve 5x faster inference speeds, and run them on scalable, high-performance serverless infrastructure.

Automated testing with Cypress has revolutionized how developers ensure code quality, but writing comprehensive test suites remains time-consuming and often tedious. Artificial intelligence tools for Cypress testing are emerging as game-changers, dramatically reducing test creation time while maintaining quality. Let’s face it—writing tests can be a real drag. After you’ve spent days coding a beautiful feature, the last thing you want to do is spend another day writing tests for it.