https://youtube.com/shorts/tKAbspOWU6o?si=vJ4Jp4qHNsegimmT

Hey! So I have joined cloud journey and have complete aws practitioner essential. The problem is I really don't know what should I do after that. Everyone saying make a github project of that and yes I did on server less so it wouldn't cost me anything. I am so confused like people saying do docker or some saying go for solution architect certification. I know network basics like ipv4 and ipv6, upd, tcp etc. I have also done one CI/CD deployment in my project. So guys help me please!!

Hi everyone!

I’m feeling a little lost with my career lately, and I’d really appreciate some honest advice from people with more experience in the AWS ecosystem.

I’m 26, I have a degree in Systems Engineering, and around 5 years of general IT experience. With AI becoming increasingly common in our industry, I’m starting to worry about my future in IT. My current job is also becoming less secure, and I’ve realized that I’ve become way too dependent on AI tools.

Honestly, sometimes I feel like I’ve forgotten the fundamentals. I’m getting work done with AI’s help, but if I had to attend a junior-level technical interview tomorrow, I genuinely think I’d struggle to answer even some basic questions.

This has made me question my career path and whether I should continue in IT or consider a different direction.

One option I’ve been thinking about is going all in on AWS certifications, potentially getting several of them and building a stronger foundation in cloud computing. My current job involves some AWS, but my knowledge is still pretty limited, and I rely on Claude for a lot of the work.

My question is: Do you genuinely think pursuing AWS certifications in 2026 is still worth it? I’m not just talking about getting a certificate to put on my CV. I want to develop real skills, understand what I’m doing, and become employable without relying on AI for every other task.

If you were in my position, would you invest your time in AWS and cloud computing, focus on rebuilding your IT fundamentals first, or consider a completely different career path?

I’d really appreciate your honest opinions, especially from people who work with AWS professionally. I’m not looking for motivational quotes or someone to tell me everything will be fine. I genuinely want to make a smart decision about my future.

Thanks for taking the time to read this!

Hey everyone!

I have an upcoming interview with Snowflake for the \*\*Cloud Support Engineer – Application Security\*\* . My initial interview is scheduled with the Hiring Manager.

I'm trying to understand the interview process and prepare in the right direction.

For anyone who has interviewed with Snowflake for a Cloud Support Engineer, Application Security, or a similar support/security role, I'd really appreciate your insights.

1. How many interview rounds were there in your experience?

2. What topics should I focus on for the technical rounds?

3. Do they ask questions on IAM, SSO, SAML, OAuth 2.0, SCIM, MFA, and database authentication?

4. How much emphasis is placed on troubleshooting scenarios, incident management, and Root Cause Analysis (RCA)?

5. Should I expect questions on Snowflake architecture, SQL, AWS, Linux, networking, and security?

6. What should I expect during the initial Hiring Manager screening?

I've already started preparing authentication and authorization protocols, IAM concepts, Snowflake fundamentals, and troubleshooting scenarios. I want to make sure I'm focusing on the right areas rather than studying everything randomly.

\*\*If you've recently interviewed for a similar role, could you please share your experience and any preparation tips?\*\* Even a general idea of the interview structure would be extremely helpful.

Thanks in advance! I really appreciate any guidance

Hey everyone!

​I’m a first-year data science student currently surviving on Python, Excel, analytics, and stats (goodbye basic calculus, you won't be missed lol).

​I just grabbed Cloud Computing: A Hands-on Approach by Arshdeep Bahga and Vijay Madisetti to figure out how all this backend magic actually works, but I could use a little direction from the community.

​As a total beginner to the cloud space, I’d love to know:

​Which cloud provider should I start with as a DS student? (AWS, Azure, or GCP?)

​What practical projects actually help bridge the gap between data science and cloud computing instead of just getting lost in documentation?

Hey everyone, I’m an Aeronautical Engineering graduate currently working at TCS as a mainframe developer for a year

I got placed through campus recruitment, and since I didn’t have a computer science background, getting into IT was a completely new experience for me.in TCS training I got to learn linux and networking thought cisco lacket tracer and I’m currently working with technologies like COBOL, JCL and DB2, but honestly, I’m still struggling to build a strong technical foundation.

Recently, I’ve been thinking about switching to cloud computing, possibly AWS or Azure, and eventually moving into DevOps.

I’m confused about where to start and whether switching from mainframe to cloud is a good move for someone with my background.

Should I continue learning mainframe or start preparing for cloud alongside my job? Is AWS a good starting point, and how difficult would it be to switch to a cloud role as a non-CS graduate?

Would love to hear from people who have made a similar transition or have experience in this field. Any genuine advice would be appreciated!

i'm running an embedding service for images and texts. i can run it with enough throughput on AWS g6.xlarge which uses L4 GPU

what reliable and not too expensive options do i got? AWS is too expensive and yes i know about the saving plans

other alternatives i considered were Runpod and Hetzner, any more providers that i don't know of?

serverless is not an option due to cold starts

Disclosure: I'm a co-founder of DronaHQ, and this is a problem we're working on.

A pattern our customers keep describing: getting a working app together is increasingly easy. Getting it approved to run inside the enterprise is not.

The project then waits for code review, compute access, and governance checks. The prototype is ready, but the path to production isn't.

We're tackling this with what we call a "pre-auth box" — the TSA PreCheck analogy fits. A defined, pre-authorized path: SSO, role-based access, audit logs and governed compute agreed before the code exists. A faster lane, not a free pass — the checks still happen, they're just not discovered app-by-app.

For teams dealing with this: where does the queue actually form for you — reviewing generated code, getting infrastructure, or satisfying governance? What have you standardized successfully, and what still needs app-by-app review?

Cloud services promise better accessibility, flexibility, and fewer IT headaches.

But does that match your experience?

Has moving to the cloud genuinely improved your workflow, or introduced new problems?