As we’ve talked about before, our app is a monolith: all our backend code lives together and gets compiled into a single binary. One of the reasons I prefer monolithic architectures is that they make it much easier to focus on shipping features without having to spend much time thinking about where code should live and how to get all the data you need together quickly. However, I’m not going to claim there aren’t disadvantages too. One of those is compile times.

$575 million was the cost of a huge IT incident that hit Equifax, one of the largest credit reporting agencies in the U.S. In September 2017, Equifax announced a data breach that impacted approximately 147 million consumers. The breach occurred due to a vulnerability in the Apache Struts web application framework, which Equifax failed to patch in time. This vulnerability allowed hackers to access the company's systems and exfiltrate sensitive data. ‍

SQL Monitor is an essential tool for DBA teams worldwide, providing real-time monitoring of SQL Server and PostgreSQL performance. With SQL Monitor, you can easily track deployments, errors, and other events on the timeline. This feature, called annotations, allows you to quickly identify the root cause of performance issues and take corrective action. SQL Monitor’s timeline is a powerful tool that helps you stay on top of your database performance and keep your systems running smoothly.

Non-Abstract Large System Design (NALSD) is an approach where intricate systems are crafted with precision and purpose. It holds particular importance for Site Reliability Engineers (SREs) due to its inherent alignment with the core principles and goals of SRE practices. It improves the reliability of systems, allows for scalable architectures, optimizes performance, encourages fault tolerance, streamlines the processes of monitoring and debugging, and enables efficient incident response.

DevOps has accelerated the delivery of software, but it has also made it more difficult to stay on top of compliance issues and security threats. When applications, environments and infrastructure are constantly changing it becomes increasingly difficult to maintain a handle on compliance and security. For fast-moving teams, real time security monitoring has become essential for quickly identifying risky changes so they can be remediated before they result in security failure.

Modern software delivery teams find themselves under constant pressure to maintain security and compliance without slowing down the speed of development. This usually means that they have to find a way of using automation to ensure robust governance processes that can adapt to evolving cyber threats and new regulatory requirements.

Prompt engineering is the practice of crafting input queries or instructions to elicit more accurate and desirable outputs from large language models (LLMs). It is a crucial skill for working with artificial intelligence (AI) applications, helping developers achieve better results from language models. Prompt engineering involves strategically shaping input prompts, exploring the nuances of language, and experimenting with diverse prompts to fine-tune model output and address potential biases.

We’ve all heard about cloud-native applications in recent years, but what about cloud-native infrastructure? Is there any reason why the infrastructure couldn’t be cloud-native, too? Or maybe it’s already cloud-native, but you’ve never had a chance to dive deep into the stack to check it out? What does the term “cloud-native infrastructure” actually even mean? The more you think about it, the more confusing it gets.

CitrixBleed, or CVE-2023-4966, is now an infamous security vulnerability affecting Citrix NetScaler that allows attackers to hijack user sessions by stealing session authentication tokens. Unfortunately, it has affected many NetScaler customers including Xfinity, which lost data for 36 million customers as a result of CitrixBleed. There is no way to protect against CitrixBleed by configuring the NetScaler WAF to detect and block it.

As rack densities in data centers increase to support power-hungry applications like Artificial Intelligence and high-performance compute (HPC), data center professionals struggle with the limited cooling capacity and energy efficiency of traditional air cooling systems. In response, a potential solution has emerged in liquid cooling, a paradigm shift from traditional air-based methods that offers a more efficient and targeted approach to thermal management.