r/EnterpriseArchitect

Just want to sanity-check my impression -- anyone else getting inundated with the vendors booking portfolio owners/VPs to do "architecture workshops", "architecture planning", "{insert bogus verb + noun} architecture"? I find SF is particularly guilty of this. They're "partnering" (yeah, right) with the enterprise, and just want to "help architect the success"... blegh.

The sessions inevitably turn into some dog-and-pony show where we have to show a nerfed view into the specific portfolio or business area, the vendor's staff trip over themselves peppering the middle management with compliments for how advanced and mature the organization is, and then pitch anything that even loosely fits as a potential solution for {insert suggested business pain point}.

I'm tired, boss.. This wastes so much time and energy. I wish vendors would find someone else to pick on!

That is all, good night.

Hi everyone!

I recently started a new role, shifting my focus away from pure software development.
To be honest, it’s a relief: I never felt coding as something fitting for me.

Currently, I’m leaning into SRE and Architecture tasks.
I’ve done similar work in the past with AWS, but now I’m diving deep into Kubernetes.
To give you some context: I’m currently helping design and implement an architecture for processing satellite data.
I have a lot of freedom in both the design phase and the implementation.

In the near future, I will also be responsible for building and managing the observability stack. Since I’m really enjoying this new stuff, I want to improve my theoretical knowledge.
I’m already taking online courses for the practical side (Kubernetes and Helm), but I feel like I'm missing the theory.

I’m looking for book recommendations on:

• System/Architecture Design: I need something that teaches best practices for designing resilient and scalable systems.
• Observability: I’m looking for a book that covers the best practices of observability, not just a manual on some specific tool.

Do you have any "must-reads" for someone in my position? Thanks!

What usually happens is simple: data that people could still work with manually stops working once AI has to read it consistently.

As I work with enterprise ecommerce, I see this pattern there clearly. Teams can live with a messy data model for a long time because people already know how to work around it. The catalog moves, search returns something, and the business keeps going.

AI changes the standard. Now the same data has to be interpreted more consistently, and that’s where old gaps start showing up.

That’s why I see the practical in not rebuilding everything and not throwing AI straight onto the raw data either. What tends to make more sense is adding a readiness layer between the existing data model and the AI use case - something that helps normalize, interpret, and prepare the data before AI starts relying on it.

That way you are not pretending the source systems are clean, but you are also not asking AI to guess its way through the mess. From my side, that’s usually a much more realistic path forward, especially in enterprise systems with lots of data and data sources.

Have you seen this too, where AI exposed data problems that day-to-day operations had been tolerating for years?

https://preview.redd.it/jbwhudd7gs0h1.png?width=1540&format=png&auto=webp&s=e39ca268dd731d4a919526db5e9d4f4cc9512207

My vote → business-first architect with strong tech grounding.

At the EA level, the harder problem is usually not coding.
It’s decomposing enterprise capabilities, aligning operating models, rationalizing systems, and coordinating transformation at scale.

How do others view this?

When I started the EA practice at my company, one piece of struggle was to build a capability map from scratch. I was fortunate to get some outside help with a consultant who provided their own, based on what they had on hand for this industry and that was enough to get us started. But what if you have nothing ? There’s no global reference catalog: it’s a bit spread apart when it exists and is sometimes gated or not in the right format.

So I decided to build my own and share it with the community for anyone struggling with that blank page :

https://catalog.turbo-ea.org

9300+ capabilities categorized by industry, 1200+ business processes, all exportable in csv to be imported in any tool.

The github repo contains the claude skill to generate them from scratch from any other industry and is self discovering/self grounding to industry standards and frameworks.

It will never be complete or perfect and has no pretention to be but it will always be better than starting with nothing.

I know there’s been a lot of companies online publicly consolidating back to a monolith. Makes sense. Centralized logging, no network hop, single process.

I think people realized there’s a lot more issues with distributed systems than originally pitched. So you need to make the cost analysis very carefully if that’s something you want to live with.

Wondering who here went back to a monolith or who here was quietly productive this whole time in a single server.

Honestly blown away by Github Copilot agent in VS code today. i just managed to create a whole data engineering structure (folders, files, loading scripts, EDA and a sales forecasting ML model). Including the deployment and I didn't write a single line of code manually.

It literally handled the entire cycle from scratch.

​Oviously i did a full expert review at the end to validate everything and make sure the logic was solid, but the fact it did all the heavy lifting is crazy.

Are we just architects now?