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Dear Architect,

Welcome back to our weekly issue!

This week, in the spotlight of the report from InfoQ about the state of DevOps and Cloud of July 2021, I'm glad to see Data Mesh in the innovators' section of the diagram.

I know many of you may not be interesting in Deno and Node.js but what was shared in that episode is a piece of history that can provide great insights to everyone out there, despite not writing JavaScript or Typescript. Moreover, this is the second show that Ryan Dahl conceded an interview in his entire career, this makes it a little bit more special.

Nick Tune provides a pragmatic way to recognise visual patterns during the design phase of our systems. Sometimes taking a step back looking at the big picture can literally help!

After the first article on Data Mesh by Zhamak, this is another top-notch article on the topic. A must-read if you ask me.

Finally, a pragmatic guide for developers who want to make it in the architecture world. Thanks, Natalia for sharing your experience.

Enjoy the read and see you next week!



DevOps and Cloud InfoQ Trends Report - July 2021

This article summarizes how we currently see the "cloud computing and DevOps" space, which focuses on fundamental infrastructure and operational patterns, the realization of patterns in technology frameworks, and the design processes and skills that a software architect or engineer must cultivate.

Case Study

Exploring Deno Land

We talk with Ryan about the massive success of Node and how it impacted his life, and how he eventually created Deno and what he’s doing differently this time around. We also talk about The Deno Company and what’s in store for Deno Deploy


Pattern Reading in Visual Discovery and Modelling

What I mean by pattern reading is looking at the visual artefacts and making inferences about what the layout may be telling us. In every workshop, I always ask everyone “Imagine there is no text here. What is this arrangement of colours and shapes telling you?”

Data Mesh

Building a successful Data Mesh

Over time, the central data function, far removed from data creation, was at a disadvantage, unable to extract most value from the data. The central data teams were several steps removed from its actual creation and weren’t the experts in the actual subject. They were experts in the data pipeline and the warehouse/lake processes but did not understand the product and transaction data they were trying to derive value out of


From developer to (solutions) architect. A simple guide.

You want to become an Architect, but you are not sure of what it takes and you want to have a better idea of what you should focus on. You want to understand where are your technical gaps and what soft skills are required. This article is for you!

Thanks for reading Dear Architects 🙏

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Have a great rest of the week 😉

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