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Running the Cloud from your Kubernetes Cluster

Running the Cloud from your Kubernetes Cluster

September 2, 2021 | Blog, Cloud, Kubernetes

In this blog, Stuart compares the new approach of deploying cloud resources as Kubernetes custom resources rather than the (now) typical approach using Terraform – or cloud specific: CloudFormation (AWS), Deployment Manager (GCP). He also identifies what resources are suitable for this approach and which ones are not.

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WRITTEN BY

Stuart Wallace

Stuart Wallace

Lead Consultant

Kafka vs RabbitMQ: The Consumer-Driven Choice

July 20, 2021 | Blog, Data Engineering, Kafka

Kafka vs RabbitMQ: The Consumer-Driven Choice

Message and event-driven systems provide an array of benefits to organisations of all shapes and sizes. At their core, they help decouple producers and consumers so that each can work at their own pace without having to wait for the other – asynchronous processing at its best.

In fact, such systems enable a whole range of messaging patterns, offering varying levels of guarantees surrounding the processing and consumption options for clients. Take for example the publish/subscribe pattern, which enables one message to be broadcast and consumed by multiple consumers; or the competing consumer pattern, which enables a message to be processed once but with multiple concurrent consumers vying for the honour—essentially providing a way to distribute the load. The manner in which these patterns are actually realised however, depends a lot on the technology used, as each has its own approach and unique tradeoffs. 

In this article we will explore how this all applies to RabbitMQ and Apache Kafka, and how these two technologies differ, specifically from a message consumer’s perspective.

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Running a Terraform Provider with a Debugger

May 19, 2021 | DevOps, Hashicorp, Open Source, Terraform Provider

Running a Terraform Provider with a Debugger

Developing a Terraform provider is a great thing for a company to do as it allows customers to quickly integrate a product with their existing systems with very little friction. During development, occasionally there might be bugs and issues to fix, and it can be quite difficult to work out what is causing them. In this post, I outline how you can attach a debugger such as Delve to a Terraform provider to save time when solving these issues.

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Machine Learning at scale: first impressions of Kubeflow

April 20, 2021 | Data Engineering, Machine Learning, Software Consultancy

Machine Learning at scale: first impressions of Kubeflow

Our recent client was a Fintech who had ambitions to build a Machine Learning platform for real-time decision making. The client had significant Kubernetes proficiency, ran on the cloud, and had a strong preference for using free, open-source software over cloud-native offerings that come with lock-in. Several components were spiked with success (feature preparation with Apache Beam and Seldon for model serving performed particularly strongly). Kubeflow was one of the next technologies on our list of spikes, showing significant promise at the research stage and seemingly a good match for our client’s priorities and skills.

That platform slipped down the client’s priority list before completing the research for Kubeflow, so I wanted to see how that project might have turned out. Would Kubeflow have made the cut?

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