Visualizing Azure Networking using D3js

2024-04-29

A picture worth a thousand words. When you work with a complex networking infrastructure, it would be great to have a bird’s-eye view of it. In this article, I want to discuss how this can be achieved using PowerShell, Jupyter notebooks, and d3js

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Continuing the Conversation on Jupyter and PowerShell

2023-10-30

Let’s dive a little deeper into the Jupyter-PowerShell duo. First, we’ll try to uncover the ‘why’ behind this alliance. Next, we’ll pry into the ‘how’ of the operation. And finally, we’ll unveil the secrets of crafting a notebook and launching it into the digital cosmos for your team or, who knows, the whole world to see. Ready? Set. Go!

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Do we always need to follow CAF recommendations?

2023-10-12

In the journey of infrastructure creation within Azure, many organizations lean towards crafting complex hub-and-spoke topologies to host their applications, even when the necessity for such complexity isn’t apparent. A common justification echoes: “It’s mandated by the Microsoft Cloud Adoption Framework (CAF)”. However, embarking on this path unfolds a myriad of related, albeit previously unexplored, domains. For instance, the newfound need to manage IP spaces, delve into VLSM subnetting - topics unfamiliar to most Dev teams. Once networked, the quest doesn’t end; it merely morphs into challenges like private DNS resolution, establishing access to internal resources, and the list trails on.

Yet, if we pause to reflect, many Azure services including Azure Web Apps were designed with a public persona. Initially, some didn’t even entertain VNET integration, and this model was well-accepted.

In this article, we aim to traverse a less convoluted route towards securely hosting Web Apps, whilst sidestepping the network-centric hurdles.

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Approach to Optimizing VM Costs

2023-10-03

Are you considering transitioning your servers from an on-prem DC to Azure cloud? One step in this journey involves mapping existing servers to their Azure counterparts. The goal is to reduce the costs of the overall bundle of boxes while obtaining the highest possible performance. In theory, we can map the source VMs to the target VMs based on the number of virtual CPUs and the amount of RAM. Although this isn’t the most challenging task, it doesn’t guarantee the highest possible performance for the lowest possible price. If we always opt for a VM with the lowest price, we may compromise on performance. Conversely, choosing VMs with the highest performance could result in higher costs. Is there a middle ground? Is it possible to achieve what we want? Let’s find out.

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