Observability refers to understanding a system’s internal state by examining its external outputs, like logs, metrics, and traces. Borrowed from control theory, it’s the ability to infer what’s happening inside a system without direct access. To visualize observability, consider the human body. We don’t need to open it up to understand its internal state. By measuring external signals like body temperature, heart rate, or blood pressure, we can infer what’s happening inside.

From humble beginnings, Apache Kafka steadily rose to prominence and now sits as the backbone of data streaming for thousands of organizations worldwide. From its robust API, cloud-native implementations like Confluent Cloud, and synergy with other technologies like Apache Flink, Kafka has grown to cover many use cases across a broad range of industries.

LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.

In the last year, we’ve seen the explosion of AI in the enterprise, leaving organizations to consider the infrastructure and processes for AI to successfully—and securely—deploy across an organization. As we head into 2025, it’s clear that next year will be just as exciting as past years. Here, Cloudera experts share their insights on what to expect in data and AI for the enterprise in 2025.

In k8s world every resource created via controller. Like there are inbuilt controllers for pods, deployments , replica set etc. So basically, Controllers are nothing but a control loop that continuously monitor the state of the cluster and take actions to bring the cluster into the desired state. The resources have a spec that provides the desired state. The controller checks the current states.

The API Waterfall is a sequentially and methodically defined approach for managing data flows and integration of services in software development. Aligned to the traditional waterfall methodology, the architectural model essentially targets a step-wise flow of events where each stage depends on the completion of the previous step.

The demand for food delivery services is skyrocketing, driven by our busy lifestyles and the need for convenience and variety. Apps like Uber Eats, Zomato, and DoorDash have changed how we order food, giving us access to countless restaurant options and smooth delivery experiences. For entrepreneurs, developing an app similar to DoorDash is a great opportunity to tap into this booming market.

According to Ruby tradition, the core team will release Ruby 3.4 on December 25—but the preview has been out since May! If you're anticipating the release like we are, you're probably wondering what's new in Ruby 3.4. There aren't any huge changes in this release, but you'll want to be aware of a few really cool things. Let's dig into some of the language changes first.

Cybersecurity is one of the top concerns for businesses today. Vulnerabilities make the news when they turn into expensive breaches. With the cloud so prevalent in today's digital landscape, much attention has turned to API security. Network attacks aimed at APIs increased by an incredible 681% in 2022. Considering the continued popularity of SOAP (simple object access protocol) APIs, it is now a top priority for global IT leaders to learn SOAP's security best practices.

When it comes to the quality of software solutions, it is often said that the best testing goes a long way. Just think about the day when you finally launched the perfect software with error-free elaborate systems that have surpassed users’ expectations and, on top of that, provide it in the sea of competitive applications—to have that is to welcome an outstanding testing partner.