Systems | Development | Analytics | API | Testing

N+1 queries are a frequent issue in complex applications built with Elixir and Phoenix. These queries can silently degrade application performance, often going unnoticed until they've compounded into a significant problem. They can substantially increase web page load times, as each additional query adds overhead to a database, consuming more time and resources. That's why it's crucial to detect and resolve N+1 queries to optimize production systems.

Some of the responsibilities of a team leader is to empower your team with the necessary tools and skill sets so that they can excel at what they do. According to the State of Quality Report 2024, some of the most prominent challenges QA teams face include a lack of time to ensure quality, difficulty in applying test automation, and frequent changes in requirements. Training and education is the key to overcoming such obstacles.

As cloud environments evolve, so must the security measures that protect them. With Confluent’s latest enhancement—AWS IAM role integration for managed connectors—you can now adopt temporary security credentials, reducing both the risk of long-term credential exposure and the operational burden of key management. This feature tightens security and simplifies access management for your data flows between AWS and Confluent Cloud.

The evolving market landscape is driving an urgent need for a unified EPM solution, as finance teams face increasing pressures from several fronts. Rapid technological advancements, heightened competition, and the growing complexity of global markets have made financial agility and real-time decision-making critical to maintaining a competitive edge.

Oracle-driven finance teams today face increasingly complex challenges. In recent years, the finance function has had to adapt to become more flexible as they navigate market upheaval, global inflation, and rapid changes to technology. This year, an Oracle survey of CFOs reveals CFO’s top challenges include navigating the need to cut costs, retaining talent within the finance function, and focusing on more accurate forecasting.

The first Rails 8 beta has officially been released, bringing an exciting set of features, bug fixes, and improvements. This version builds on the foundation of Rails 7.2, while introducing new features and optimizations to make Rails development even more productive and enjoyable. Key highlights include an integration with Kamal 2 for hassle-free deployments, the introduction of Propshaft as the new default asset pipeline, and extensive ActiveRecord enhancements.

Whether or not you're active in the Rails ecosystem, you might already have heard some of the buzz around Solid Queue, a new database-backed backend for ActiveJob. Solid Queue is a simple and performant option for background jobs that lets you queue large amounts of data without maintaining extra dependencies like Redis. We've already talked about how to deploy, run, and monitor Solid Queue, but we haven't yet explored how Solid Queue works.

Although important to see alternatives to iframes, iframes are still a valuable and commonly applied method for successful embed frameworks. And even if considering alternatives, iframes help to understand possibilities, along with design and technical details to consider for a successful platform. There’s more to embed Frameworks than letting partners appear in your interface.

Whether you’re a Software Developer, DevOps Engineer, or Quality Assurance (QA) specialist, mastering API testing with tools like Postman is essential, especially during development. API testing using Postman makes it possible to increase security, provide better user experiences, and minimize the possibility of losses through bugs or vulnerabilities.

When scaling data science and ML workloads, organizations frequently encounter challenges in building large, robust production ML pipelines. Common issues include redundant efforts between development and production teams, as well as inconsistencies between the features used in training and those in the serving stack, which can lead to decreased performance. Many teams turn to feature stores to create a centralized repository that maintains a consistent and up-to-date set of ML features.