Bringing databases and Kubernetes together

A supply chain attack on SAP-related npm packages has put fresh scrutiny on the developer tools and build workflows that enterprises rely on to produce software. The campaign, referred to as “mini Shai-Hulud,” affected packages used in SAP’s JavaScript and cloud application development ecosystem. The malicious versions added installation-time code that could steal developer credentials, GitHub and npm tokens, GitHub Actions secrets, and cloud credentials from AWS, Azure, GCP, and Kubernetes environments. Researchers at SafeDep, Aikido Security, Wiz, and several other security firms said the affected packages included mbt@1.2.48, @cap-js/db-service@2.10.1, @cap-js/postgres@2.2.2, and @cap-js/sqlite@2.2.2. The suspicious versions were published on April 29 and were later replaced by safe releases. The malware encrypted stolen data and sent it to public GitHub repositories created from victims’ own accounts, according to the researchers. It also used stolen GitHub and npm tokens to add ma

In The Sorcerer’s Apprentice, Mickey Mouse uses a magic spell to do his chores. The spell animates a broom that is tasked with carrying water from the well. While the animated broom is managed, it gets the job done; when Mickey falls asleep, the broom carries on its work. When Mickey can’t stop the broom, he chops it to bits with an axe, but all the pieces re-animate and carry on as before. Finally the Sorcerer intervenes to stop the broom and clean up the mess. Similarly, AI promises to lighten the burden of operating databases. For example, using AI to write SQL queries or optimize performance are obvious areas to apply this technology. There is a huge amount of SQL on the internet that can be used to train models around what good queries should look like, and transforming natural language into accurate SQL has a lot of promise. Further, using AI to handle database management issues should deliver faster performance, more reliable systems, and more efficient use of resources. Custome

Today on Decoder, I want to lay out an idea that’s been banging around my head for weeks now as we’ve been reporting on AI and having conversations here on this show. I’ve been calling it software brain, and it’s a particular way of seeing the world that fits everything into algorithms, databases and loops — software. Software brain is powerful stuff. It’s a way of thinking that basically created our modern world. Marc Andreessen, the literal embodiment of software brain, called it in 2011 when he wrote the piece “Why software is eating the world” as an op-ed in The Wall Street Journal. But software thinking has been turbocharged by AI in a way that I think helps explain the enormous gap between how excited the tech industry is about the technology and how regular people are growing to dislike it more and more over time. In fact, the polling on this is so strong, I think it’s fair to say that a lot of people hate AI. And Gen Z in particular seems to hate AI more and more as they enco

For years, Kubernetes held an almost mythic place in enterprise IT. It was positioned as the control plane for the future, the standard abstraction for cloud-native systems, and the platform that would finally free enterprises from infrastructure lock-in. To be fair, some of that was true. Kubernetes brought discipline to container orchestration, enabled portable deployment models, and provided architects with a powerful framework for managing distributed applications at scale. However, the market is changing, and so are enterprise expectations. The question is no longer whether Kubernetes is technically impressive. It clearly is. The question is whether it still represents the best fit for a growing number of mainstream enterprise use cases. In many cases, the answer is increasingly no. What we are seeing is not the death of Kubernetes but the end of its unquestioned dominance as the default strategic choice. Here’s why. Too operationally expensive As Kubernetes adoption grew, many o

If you’re using Kubernetes, especially a managed version like Azure Kubernetes Service (AKS), you don’t need to think about the underlying hardware. All you need to do is build your application and it should run, its containers managed by the service’s orchestrator. At least that’s the theory. However, implementing a platform that abstracts your code from the servers and network that support it brings its own problems, and a whole new discipline. Platform engineers fill the gap between software and hardware, supporting security and networking, as well as managing storage and other key services. Kubernetes is part of an ecosystem of cloud-native services that provide the supporting framework for running and managing scalable distributed systems, including the tools needed to package and deploy applications, as well as components that extend the functionality of Kubernetes’ own nodes and pods. Key components of this growing ecosystem are the various service meshes. These offer a way to m

Juro has launched Operator, which provides natural language ‘conversational access to a business’s contracts and data’ in order to surface key information. The inhouse-focused company ...

Learn seven Docker Compose templates for CMS, web apps, databases, Python backends, streaming, automation, and local AI development.