Rob Gibbon

The purpose of this guide is to highlight the key differences between Apache Spark 3 and Spark 4, and provide advice on how to plan a migration. Let’s get started. The biggest changes Let’s talk about the biggest changes between Apache Spark 3.x and Spark 4. Scala 2.12 no more First up, there’s no support

What is llm-d? llm-d is an open source solution for managing high-scale, high-performance Large Language Model (LLM) deployments. LLMs are at the heart of generative AI – so when you chat with ChatGPT or Gemini, you’re talking to an LLM. Simple LLM deployments – where an LLM is deployed to a single server – can

Many of us are familiar with the retrieval augmented generative AI (RAG) pattern for building agentic AI applications – like digital concierges, frontline support chatbots and agents that can help with basic self-service troubleshooting.  At a high level, the flow for RAG is fairly clear – the user’s prompt is augmented with some relevant

Apache Spark is a popular framework for developing distributed, parallel data processing applications. Our solution for Apache Spark on Kubernetes has made significant progress in the past year since we launched, adding support for Apache Iceberg, a new GPU accelerated image using the NVIDIA Spark-RAPIDS plugin, and support for the Volcan

Move over Hadoop, it’s time for Spark on Kubernetes Apache Spark, a framework for parallel distributed data processing, has become a popular choice for building streaming applications, data lake houses and big data extract-transform-load data processing (ETL). It is horizontally scalable, fault-tolerant, and performs well at high scale. H

It’s all about AI these days, so I decided to try and answer the important question: can you make a Spark cluster run AI agents that play a game of Doom, in a multiplayer LAN party? Although I’m no data scientist, I was able to get this to work and I’ll show you how so

Download the Spark reference architecture guide In this post we’ll explore deploying a fully operational, on-premise data hub using Canonical’s data centre and cloud automation solutions MAAS (Metal as a Service) and Juju. MAAS is the industry standard open source solution for provisioning and managing physical servers in the data centre.

In the early 2010s, Apache Hadoop captured the imagination of the tech community. A free and powerful open source platform, it gave users a way to process unimaginably large quantities of data, and offered a dazzling variety of tooling to suit nearly every use case – MapReduce for odd jobs like processing of text, audio

Charmed Kafka is a complete solution to manage the full lifecycle of Apache Kafka. The Canonical Data Fabric team is pleased to announce the first beta release of Charmed Kafka, our solution for Apache Kafka®. Apache Kafka® is a free, open source message broker for event processing at massive scale. Kafka is ideal for building

We’re super excited to announce that we have shipped the first release of our solution for big data – Charmed Spark. Charmed Spark packages a supported distribution of Apache Spark and optimises it for deployment to Kubernetes, which is where most of the industry is moving these days. Reimagining how to work with big data

I’ve read and watched more than a few articles about ChatGPT in the last couple of months. It seems the large language model AI hype machine just can’t stop.  As somebody with a passion for music production, some of the more interesting things I’ve seen included a guy using ChatGPT to build a virtual effect

The Canonical Data Fabric team is pleased to announce the first beta release of Charmed Spark, our solution for Apache Spark. Apache Spark is a free, open source software framework for developing distributed, parallel processing jobs. It’s popular with data engineers and data scientists alike when building data pipelines for both batch an

We’ve all read the headlines about spectacular data breaches and other security incidents, and the impact that they have had on the victim organisations. And in some ways there’s no place more vulnerable to attack than a big data environment like a data lake.

Designing a production service environment around Apache Kafka that delivers low latency and zero-data loss at scale is non-trivial. Indeed, it’s the holy grail of messaging systems. In this blog post, I’ll outline some of the fundamental service design considerations that you’ll need to take into account in order to get your service arch

Software operators are steadily revolutionising how we deploy and run complex distributed systems. They offer the promise of low-intervention, self-driving software – ideally leading to service reliability gains and better uptime. For an introduction to Kubernetes operators, check out our introductory webinar or download our guide to Kube

Canonical Data Platform: that was 2021 It’s that time of the year again: many folks are panic buying cans of windscreen de-icer spray and thermal underwear, bringing pine trees into the front room and preparing to enjoy an extended break with the family. So we thought to ourselves, what better time than now to take