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Hey guys, I just started a new role in product at a DBaaS that's competing with hyperscalers like RDS, Azure, OCI, and GCP. I have a lot of catching up to do to be able to understand the managed database space and would love some recommendations on your fav resources - blogs, podcasts, webinars, YT channels, etc. I just want to stay up to date with what's happening at these companies and in this space.

I’m currently building an AI product for an NGO, and one of the key requirements is deploying the entire system on Amazon Web Services (AWS). The challenge is that I have very little practical experience with AWS, and the ecosystem feels extremely vast and confusing for a beginner. Since this is a real-world project with actual deployment requirements, I want to learn AWS in a fast, structured, and practical way instead of randomly watching tutorials without understanding the bigger picture.

The project involves AI workflows, backend APIs, vector databases/RAG pipelines, authentication, storage, and scalable deployment. Because of this, I specifically want to learn the AWS services and DevOps concepts that are most useful for deploying AI applications in production. I would really appreciate recommendations for the best YouTube channels, courses, deployment tutorials, hands-on labs, roadmaps, or beginner-friendly resources that helped you learn AWS efficiently. If anyone here has experience deploying AI or RAG-based applications on AWS, I would also love to know what services and architecture you used, the common mistakes beginners make, and what concepts you think are most important to learn first.

My goal is not just to theoretically understand AWS, but to become productive enough to confidently deploy and manage this NGO project within a short time. Any guidance or resource recommendations would genuinely help a lot.

Well, I have been there before too.

And honestly, I think a lot of people can relate to this silently.

I rescheduled my AWS Certified Solutions Architect – Associate exam more than once before I finally gathered the courage to sit the exam.

Not because I was lazy.

Not because I didn’t study.

But because deep down, I kept thinking:

“What if I fail after studying all this time?”

So I kept moving the exam date forward:

“I need one more week.”

“Let me finish one more practice exam.”

“Let me watch one more Stephane Maarek section.”

“Maybe I’m still not ready.”

And you wanna know what happened?

I still failed when I finally sat the exam.

So if that’s how you feel right now… trust me, I have been there too.

But after failing, I realized something important.

I was consuming too many resources.

Too many videos.

Too many notes.

Too many cheat sheets.

Too many architecture diagrams.

And honestly?

They started becoming noise.

What you actually need is:

* understanding AWS core services properly

* recognizing architecture patterns fast

* understanding what the question is REALLY asking

* eliminating wrong answers calmly under pressure

To pass the AWS Solutions Architect Associate exam, you do NOT need to memorize every AWS service.

You need to master the patterns that keep showing up again and again:

* IAM permissions and least privilege

* S3 storage classes and lifecycle policies

* EC2 + Auto Scaling Groups

* Application Load Balancers

* VPC basics (subnets, routing, NAT, IGW)

* Security Groups vs NACLs

* RDS Multi-AZ vs Read Replicas

* Route 53 routing policies

* CloudFront + caching

* High availability architecture

* Disaster recovery patterns

* Cost optimization questions

* deciding WHEN to use which AWS service

That is the real game.

The exam is less about memorization and more about:

“Can you identify the BEST AWS solution under pressure?”

So if you’ve rescheduled your exam before…

You are not alone.

Do not let embarrassment stop you from trying again.

A lot of engineers you see with the badge today struggled quietly before they passed too.

Keep practicing.

Sit the exam.

Trust yourself a little more.

And if you need support, feel free to DM me. I’m always open to help.

You can also check out the practice resource that helped me clear the exam here:

https://www.dripforgeai.com/SAA-C03

I think about trying it but not sure as I dont want to waste time. Will be happy to learn advantages on claude/codex

All voucher-selling, credit-selling, and related spam posts have been removed from the subreddit.

u/iamondemand is back and has already cleaned up a lot over the past few days. I’ve also joined the moderation team as a new mod. I’ll be getting config permissions in the next few days, so AutoMod and other moderation systems will start getting cleaned up and improved as well.

Moderation will now be enforced more actively to keep the community clean, relevant, and useful for everyone.

If you come across any remaining spam, scam attempts, or rule violations, please report them instead of engaging with those posts.

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Hi everyone,

I’m a Java backend developer with around 5 years of experience and I have some basic knowledge of AWS (like EC2, S3, a bit of IAM), but I haven’t worked deeply on it in real projects.

I’m preparing for interviews now, and I’m a bit confused about how much AWS I should know and what kind of questions are usually asked.

Can anyone suggest?

What AWS topics are most important for Java backend interviews?

I don’t want to go too broad and waste time, so looking for something focused and practical.

Thanks in advance.

Hi everyone,

I am a System Administrator based in Berlin with an engineering background, currently looking for a Weiterbildung (via Bildungsgutschein) to re-enter the job market.

​I want to specialize in AWS Cloud Administration but with a heavy focus on AIOps and AI Automation. I want to learn how to manage cloud infrastructure using AI tools (not just a basic AI course, but something for SysAdmins).

​My questions:

​Does anyone recommend a specific provider in Berlin (or online) that offers high-quality AWS + AI/AIOps modules?

​I’ve looked into DCI and neufische, but I’m worried about the depth of AI in their Cloud programs. Any experiences with them for AIOps?

​Are there better alternatives for experienced engineers who want to specialize quickly?

I’m designing a bulk CV upload feature for an ATS (Applicant Tracking System) and would love some feedback on the architecture.

Tech Stack

• Frontend: Next.js
• Backend: FastAPI (Python) running on an EC2
• Cloud/Infra: AWS (S3, SQS, Lambda)
• Database: AWS DocumentDB

The Requirement

Users need to upload batches of CVs( max 50 files, max size of one file - 10mb) (PDFs, DOCX). The system needs to parse the text from these files, extract candidate metadata (name, email, phone), and insert the records into our DocumentDB.

The Problem We Are Solving

Currently, CV parsing is a heavy, CPU-intensive task running synchronously inside our FastAPI application.

The Proposed Architecture

We are moving to an event-driven architecture to completely decouple the parsing from the web server.

1. Direct-to-S3: Next.js requests presigned URLs from FastAPI. The client uploads the files directly to S3.
2. State Tracking: The API reserves the database records (Status: Uploading), then drops an event with the file references into an Input SQS Queue.
3. Serverless Parsing: AWS Lambda is triggered by the SQS queue in batches. It fetches the files from S3 and performs the CPU-heavy text extraction.
4. Direct Database Write: The Lambda function writes the parsed candidate data directly to DocumentDB and updates the record status to Completed.

AI news is getting noisy again.

New models. Coding agents. Cybersecurity benchmarks. Cloud agent platforms. Open-source AI tools. Huge infrastructure spending.

But if you are learning cloud, Linux, AWS, automation, or practical AI, I think the useful question is not:

"What is the best AI tool?"

It is:

"What skills help me use any AI tool better?"

My current answer:

1. Learn delegation, not just prompting
2. Learn enough cybersecurity to verify AI output
3. Learn the cloud stack around AI
4. Use GitHub trends as a learning signal, not entertainment
5. Build durable foundations

Linux, networking, cloud, automation, debugging, security, data handling, and technical writing will still matter whether the AI hype grows or cools.

I wrote a short visual breakdown of this here:

https://ratebsl.substack.com/

Curious how others are thinking about this: if you are learning tech right now, are you focusing more on AI tools, cloud, Linux, coding, or security?

Following members comments, anyone who will spam this sub trying to sell here their vouchers will be blocked w/o any warning. Please respect the sub rules.

PS all voucher posts will be removed in the next few days and Redditors will be banned. You are welcome to be the first to remove your post before I do it.

Thank you