Lambda Architecture - A data engineering approach for big data
Lambda architecture is a data processing architecture designed to handle large amounts of data by combining batch processing with real-time stream processing.Lambda architecture provides a way to handle both real-time and batch processing in a single architecture.
Meet OpenBao, an Open Source Fork of HashiCorp Vagrant
First Terraform, and now Vault: More open source code left by HashiCorp is a finding a home with potential competitors. This time, IBM is sniffing around the spoils.
Spritely and Veilid: Exciting Projects Building the Peer-to-Peer Web
While there is a surge in federated social media sites, like Bluesky and Mastodon, some technologists are hoping to take things further than this model of decentralization with fully peer-to-peer applications. Two leading projects, Spritely and Veilid, hint at what this could look like.There are...
ScaleOut Software Releases Open-Source APIs for Digital Twins and Development Workbench : @VMblog
ScaleOut Software announced the first open-source release of its digital twin APIs and development workbench for its ScaleOut Digital Twins hosting platform.
At OpenAI I have spent the last few months of my life developing a Kubernetes scheduler plugin to customize preemption to better suit our ML workloads.
What is Attribute Based Access Control (ABAC)? | Permit
Learn about Attribute-Based Access Control (ABAC) - advanced authorization model using attributes over roles for precise application security and access control
Chatbot users often recommend treating a series of prompts like a conversation, but how does the chatbot know what you're referring back to? A new study reveals the mechanism used by transformer models—like those driving modern chatbots—to decide what to pay attention to.
Visualizing Uncertainty for Non-Expert End Users: The Challenge of the Deterministic Construal Error
There is a growing body of evidence that numerical uncertainty expressions can be used by non-experts to improve decision quality. Moreover, there is some evidence that similar advantages extend to graphic expressions of uncertainty. However, visualizing uncertainty introduces challenges as well. Here, we discuss key misunderstandings that may arise from uncertainty visualizations, in particular the evidence that users sometimes fail to realize that the graphic depicts uncertainty. Instead they have a tendency to interpret the image as representing some deterministic quantity. We refer to this as the deterministic construal error. Although there is now growing evidence for the deterministic construal error, few studies are designed to detect it directly because they inform participants upfront that the visualization expresses uncertainty. In a natural setting such cues would be absent, perhaps making the deterministic assumption more likely. Here we discuss the psychological roots of this key but underappreciated misunderstanding as well as possible solutions. This is a critical question because it is now clear that members of the public understand that predictions involve uncertainty and have greater trust when uncertainty is included. Moreover, they can understand and use uncertainty predictions to tailor decisions to their own risk tolerance, as long as they are carefully expressed, taking into account the cognitive processes involved.
Kafka Monitoring with eBPF: It’s a Whole New Perspective
Find out why comprehensive monitoring of your Kafka performance is critical and why standard approaches like server-side monitoring can undercut visibility into Kafka performance – even if you don't realize it until it's too late. Get the tips you need to improve your Kafka monitoring strategy by leveraging tools like eBPF.
