What’s New

GPT-5.2 arrives with key upgrades:

• Deeper reasoning that handles multi-step logic and tough technical problems.

• Stronger context handling, letting the model work with large inputs like codebases, long project briefs, and detailed meeting notes.

• Agentic execution, so it can generate shippable assets in one flow—design docs, code, tests, deployment scripts—without constant back-and-forth.

• Enterprise-grade safety, including managed identities and policy controls through Foundry.

Two models are available:

• GPT-5.2 – best for advanced reasoning and polished work like reports, spreadsheets, and slides.

• GPT-5.2-Chat – tuned for daily tasks, how-to guidance, explanations, and learning.

Why It Matters

This release aims at teams building real AI agents, not simple chat assistants. GPT-5.2 helps with:

• Long-running workflows that need traceable, structured output

• Auditable code generation

• Complex planning and decision support

• Large-scale modernization work across apps and data pipelines

• Context-aware customer support agents

Use cases span financial services, healthcare, manufacturing, and any industry that needs reliable AI handling high-stakes tasks.

My Take

This feels like a step toward AI that can work more like a full project partner. The reasoning upgrades and end-to-end task execution are especially helpful for engineering teams that want fewer iteration loops and clearer outputs. It also helps enterprises build agents they can actually trust in regulated environments.

If you’re already using Foundry, this is a strong upgrade worth testing with real workflows.

That’s it for today.

I break down AI, cloud, DevOps automation, and the trends shaping the next generation of engineering, all in one place.

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The Reality of Trends

Every week, new tools and frameworks appear. Many fade within months. Engineers who adopt blindly waste time and risk breaking existing systems.

What Really Matters

• Test trends in small, safe environments

• Measure impact on speed, reliability, and cost

• Adopt only what solves tangible problems

Practical Steps

• Track updates from AWS, Terraform, and DevOps communities

• Prioritize tools that automate pain points

• Document lessons learned and share with teammates

My Takeaway

Staying current isn’t about chasing hype. It’s about selective adoption with measurable value.

Stay ahead of the cloud curve. I share AWS/Azure news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, two times a week, all in one place.

Whenever you’re ready, here are 3 ways I can help you:

1. Career Advice: Ready to advance your AWS DevOps & Cloud Engineering career? Get personalized career guidance and expert resume reviews that land interviews. Let’s accelerate your success. Get in touch today.

2. Technical Content Development: Let’s collaborate on technical content for your business. I create custom blogs, hands-on labs, and interactive workshops tailored to your team’s needs. Transform learning into result. Book a FREE session today.

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This week’s spotlight is on the massive batch of announcements that landed during AWS re:Invent 2025 and why they matter.

What’s New

AWS introduced a series of heavyweight capabilities across Support, Lambda, S3, AI infrastructure, and autonomous engineering agents.
Here are the 10 standout updates shaping the future of cloud operations:

1. New AI-Enhanced AWS Support Plans

AWS Support now blends AI-powered diagnostics with expert human guidance, helping teams identify issues before they hit production.
This shifts support from reactive troubleshooting to proactive system health.

2. Database Savings Plans, Up to 35% Cheaper

A new pricing model lets teams save up to 35% on database spend by committing to predictable usage over one year.
A simple, flexible way to reduce RDS and Aurora costs without refactoring.

3. Lambda Durable Functions for Multi-Step Workflows

Lambda can now handle long-running, multi-step, and fault-tolerant workflows that pause and resume without Step Functions.
Great for order pipelines, approval workflows, and AI agent tasks.

4. Amazon S3 Now Supports 50 TB Objects

You can store single files up to 50 TB, eliminating the need for chunking large datasets, training data, backups, or media assets.

5. Amazon S3 Vectors, 2 Billion Vectors per Index

S3 Vector Indexes now scale to 2B vectors per index, with improved performance and lower cost.
Perfect for RAG, semantic search, and large-scale similarity workloads.

6. S3 Batch Operations, Up to 10× Faster

Bulk copy, tagging, and lifecycle tasks now run up to 10× faster, even across datasets with tens of billions of objects with no configuration changes.

7. AWS AI Factories for On-Prem AI

AWS now allows enterprises to deploy full AI infrastructure inside their own data center, including Trainium, NVIDIA GPUs, fast networking, Bedrock, and SageMaker.
On-prem meets hyperscale AI.

8. Kiro Autonomous Agent

Kiro is a fully autonomous engineering agent that:

• writes code

• runs tests

• coordinates across repos

• opens pull requests
  and works continuously for hours or days.

A new category of hands-off software execution.

9. AWS Security Agent

An AI-driven security layer that reviews architectures, scans code, and performs continuous security analysis across the SDLC.
Designed to catch vulnerabilities early.

10. AWS DevOps Agent

An autonomous operator that triages incidents, finds root causes, and recommends improvements across AWS, hybrid, and multi-cloud environments.
Essentially, a tireless DevOps engineer.

Why This Matters

These updates signal a major shift:

• AI-native operations across support, security, DevOps, and development

• Massive improvements in S3 scalability and performance

• Cheaper, more flexible database economics

• Easier orchestration of multi-step workloads with Lambda

• Enterprise-grade AI running on-prem, fully AWS-integrated

This is AWS leaning into automation, scale, and hybrid AI, all at once.

My Take

AWS didn’t just release features, they pushed the cloud closer to autonomous operations.

• Lambda durable functions reduce the need for Step Functions in many workflows.

• S3’s upgrades (50 TB objects, 2B-vector indexes, faster batch jobs) unlock new data patterns.

• AI Factories show AWS is serious about hybrid AI infrastructure.

• And the new AWS engineering agents (Kiro, Security, DevOps) hint at a future where cloud maintenance is increasingly automated.

Quiet or loud, these updates will reshape how teams build and run systems in 2025 and beyond.

Subscribe to my newsletter and stay ahead of the cloud curve. Every week, I share AWS news, Azure feature releases, and the most talked-about DevOps trends, all in one place.

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Subscribe now

Whenever you’re ready, here are 5 ways I can help you:

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2. Cloud Consulting: Facing AWS challenges? Get expert guidance on architecture, cost optimization, DevOps or infrastructure decisions. Book a call and let’s find the best solution together.

3. Career Advice: Ready to advance your AWS DevOps & Cloud Engineering career? Get personalized career guidance and expert resume reviews that land interviews. Let’s accelerate your success. Get in touch today.

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The Misconception About Automation

Many engineers equate writing a script with automating processes. The truth? Automation is only valuable when:

• It’s repeatable

• It’s maintainable

• It reduces toil for your team

What Separates Practitioners from Learners

• Testing automation like production code

• Designing for collaboration, not just execution

• Monitoring and iterating continuously

Practical Steps

• Write Terraform modules that are reusable and modular

• Automate repetitive tasks (provisioning, scaling, cleanup)

• Pair Infra-as-Code with monitoring to prevent drift

My Takeaway

Automation is a craft. Engineers who design workflows that scale safely and reliably are the ones who multiply their team’s impact.

Subscribe now

Stay ahead of the cloud curve. I share AWS/Azure news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, two times a week, all in one place.

Whenever you’re ready, here are 3 ways I can help you:

1. Career Advice: Ready to advance your AWS DevOps & Cloud Engineering career? Get personalized career guidance and expert resume reviews that land interviews. Let’s accelerate your success. Get in touch today.

2. Technical Content Development: Let’s collaborate on technical content for your business. I create custom blogs, hands-on labs, and interactive workshops tailored to your team’s needs. Transform learning into result. Book a FREE session today.

3. Cloud Consulting: Facing AWS challenges? Get expert guidance on architecture, cost optimization, DevOps or infrastructure decisions. Book a call and let’s find the best solution together.

AWS just introduced a helpful new capability in Amazon CloudWatch: you can now enable deletion protection on your log groups.

What’s New

This feature protects your critical logs from being removed by mistake. Once deletion protection is turned on, a log group can’t be deleted until you manually disable the setting.

It works across all AWS commercial Regions and can be applied during log group creation or added later. You can manage it through the CloudWatch console, AWS CLI, CDK, or SDKs.

Why This Matters

This update brings:

• Extra safety for audit, compliance, and operational logs

• Stronger guardrails against accidental data loss

• More controlled retention for production logs used in investigations and debugging

For teams handling sensitive workloads or strict compliance requirements, this is a straightforward but meaningful improvement.

My Take

This is a solid quality-of-life update for engineers and security teams. It reduces the risk of losing important logs and gives organizations tighter control over data that must be preserved.

That’s it for this week.

Until then, stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

Before, I used to rely on tutorials and docs. They look impressive on a resume, but when incidents happen at work, theory fails me.

Here’s what tutorials and docs didn’t teach me

• Direction without patterns when there are system failures

• Scripts not tested in production-like environment

• Building steps but no clear systems to debug issues

This is what I did to reclaim my expertise

• Reviewed postmortems of failures:

  I went back to any postmortems documentation I could find, both within the company and externally, to study them.

• Reproduced issues in safe sandboxes:

  Every time someone else fixed an issue, I recreated it in a sandbox environment. This helped me learn to troubleshoot it in case it happened again.

• Documented fixes to prevent recurrence:

  I actively contributed to documentation for each incident, ensuring the steps and lessons were clear for future reference.

This is what you can do if you’re learning to debug production issues

• Build small test environments to simulate failure

• Log all incidents and create knowledge artifacts

• Pair debug sessions with teammates to cross-pollinate learning

My Take

If can confidently troubleshoot incidents, you become invaluable as an engineer. Training alone doesn’t prepare you for the chaos of production, experience does.

That’s it for this week. Thanks for reading.

• If you found this useful, like, comment, share and subscribe.

• Have a question or topic you’d like me to cover in a future issue? Hit reply, I’d love to hear from you.

Since then, stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

Terraform has released v1.14.0, and this one brings meaningful improvements to how we explore infrastructure and run small operations during deployments.

What’s New

Terraform v1.14.0 introduces two standout capabilities:

1. List Resources

You can now define list queries in .tfquery.hcl files and use a new command to explore your existing infrastructure. This gives teams a clearer way to audit and understand what’s already deployed without workarounds or custom scripts.

2. Actions

Terraform now supports provider-defined “Actions,” which let you run tasks like invoking a Lambda or triggering a CloudFront invalidation directly in your configuration. These aren’t full resources—they’re small operational tasks you can attach to your workflows.

The release also includes several fixes, testing improvements, and added support for the AWS European Sovereign Cloud.

Why This Matters

This update brings:

• Easier infrastructure discovery through built-in queries

• The ability to run operational tasks without stepping outside Terraform

• Cleaner workflows, especially for teams managing large multi-cloud environments

• More predictable testing and validation behavior

These changes reduce the friction around day-to-day Terraform use and help teams move faster with fewer hacks.

My Take

Terraform v1.14.0 feels like a practical upgrade focused on real engineering pain points.

• Built-in querying is something many teams have been waiting for.

• Actions are a nice step toward blending declarative infrastructure with the small tasks we need during deployments.

Overall, this release is a quiet but meaningful improvement.

If you want weekly cloud, DevOps, and Terraform insights delivered straight to you, subscribe here.

Stay ahead of the cloud curve. Every week, I share AWS news, feature releases, and the most talked-about DevOps trends, all in one place.

This is Part II of our production-ready roadmap for learning AWS and Cloud Engineering.

If you missed Part I, you can read it here.

Let’s continue:

4. Specialize: Build Real AWS Architectures (Production Patterns)

Go beyond tutorials. Build architectures real companies use today.

Focus on:
→ Event-driven architectures (SQS, SNS, EventBridge)
→ API-driven services (API Gateway, Lambda, ECS)
→ Secure networking (PrivateLink, Transit Gateway)
→ Data storage patterns (DynamoDB, Aurora, S3 intelligence)

Top Courses I Recommend:

• AWS Architecting Specialization
  https://www.coursera.org/specializations/aws-architect

• AWS Well-Architected Framework
  https://www.pluralsight.com/courses/aws-well-architected-framework

• AWS Serverless Microservices with AWS Lambda & API Gateway
  https://www.udemy.com/course/aws-serverless-a-complete-guide

5. Build End-to-End Production Projects (This Is Where It Clicks)

If you want to stand out, you need projects that look like real work.

Build projects like:
→ A serverless API with Lambda, DynamoDB, API Gateway
→ CI/CD pipeline with GitHub Actions + AWS CodeBuild
→ A VPC-secure three-tier architecture
→ A multi-region disaster-recovery setup
→ A cost-optimized data lake with S3 + Athena

Top hands-on resources:

• Real AWS Scenarios & Challenges

• Hands-On AWS Troubleshooting

• Real-World AWS Projects (IaC + Serverless)

6. Learn Cloud Operations, Security & MLOps (Production Reality)

This is what separates hobbyists from real cloud engineers.

Focus on:
→ Monitoring, logging, incident response
→ IAM security, least privilege, access boundaries
→ Scaling, load balancing, blue/green deployments
→ Cost optimization
→ MLOps for production AI/ML apps (SageMaker, CI/CD, inference)

Top Resources:

• AWS Cloud Security
  https://www.coursera.org/specializations/aws-security

• AWS Monitoring & Observability
  https://www.udemy.com/course/aws-monitoring

• Pluralsight – AWS Networking & Security
  https://www.pluralsight.com/paths/aws-networking-security

• DevOps and AI on AWS Specialization
  https://www.coursera.org/specializations/devops-ai-aws

My Take

Most people stop after learning AWS basics. Some stop at serverless.
Almost everyone stops before Ops + Security + Architecture.

You don’t need every resource. Pick the ones aligned with your goals.

But if you want production-ready AWS Cloud Engineering skills, don’t skip the engineering fundamentals.

Share this with someone who needs a production-focused AWS roadmap.

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Stay tuned for more updates. I cover cloud news, AWS releases, AI/ML changes, Terraform automation, and the trends shaping DevOps—three times a week, all in one place.

I served as the QA Testing & Reporting Lead on the OSDU project for a Fortune 100 company while working at one of the Big 5 consulting firms.

At the time, I had experience in program management, system administration, and AWS cloud advisory. However, I realized that if I wanted to lead a team of cloud engineers, architects, and cybersecurity analysts, I needed to become more technical.

In my off hours, I created a practical roadmap to help both myself and the team continue building and improving our OSDU SaaS platform.

I taught myself AWS Cloud Engineering (Part I)

If you’re learning AWS without preparing for production, you’re wasting time.

Here’s the production-ready roadmap I’d follow:

1. Master Linux, Python & Automation (Your Core Foundation)

If you can’t automate, you can’t scale.
If you can’t script, you can’t operate in AWS.

First thing first, focus on:
→ Linux fundamentals, shell scripting
→ Python for automation
→ Git, troubleshooting, debugging

Top Courses I Recommend:

• Linux for Beginners: Linux Basics
  https://www.udemy.com/course/linux-for-beginners-2021/

• Automate Everything with Python
  https://www.udemy.com/course/automate-everything-with-python

• Google IT Automation with Python
  https://www.coursera.org/professional-certificates/google-it-automation

• The Git & Github Bootcamp

  https://www.udemy.com/course/git-and-github-bootcamp/

2. Build a Strong AWS Foundation (The Part Everyone Must Master)

Know the fundamentals before jumping into fancy architectures.

Focus on:
→ IAM, VPC, EC2, S3, RDS, Lambda
→ Networking, security, identity, monitoring
→ Understanding shared responsibility model

Resources I used:

• AWS Cloud Practitioner
  https://www.coursera.org/learn/aws-cloud-practitioner-essentials

• Ultimate AWS Certified Solutions Architect (Stephane Maarek)
  https://www.udemy.com/course/aws-certified-solutions-architect-associate-saa-c03

• AWS Networking Deep Dive
  https://www.pluralsight.com/cloud-lab/aws-networking

3. Hands-On with Cloud Architecture & DevOps

This is where good engineers become great engineers.

Focus on:
→ CI/CD pipelines
→ Containers & serverless patterns
→ Infrastructure as Code (Terraform or CloudFormation)
→ Observability (CloudWatch, X-Ray, logging)

Top Resources:

• Udemy – DevOps Bootcamp: Terraform, GitHub Actions, Docker

• AWS DevOps Engineer Learning Path

• Automate Infrastructure on AWS with CloudFormation
  

You don’t need every resource listed on this roadmap. Pick the ones aligned with your goals.

But if you want production-ready AWS Cloud Engineering skills, don’t skip the engineering fundamentals.

That’s it for Part I.

Subscribe to my newsletter and receive future articles directly to your inbox.

You can find Part II here.

Stay tuned for more updates. I cover cloud news, AWS releases, AI/ML changes, Terraform automation, and the trends shaping DevOps—three times a week, all in one place.

Kubernetes has announced plans to retire Ingress NGINX in March 2026. Until then, it’ll only receive light maintenance. After that point, there will be no updates, no fixes, and no security patches. Your existing deployments will keep running, and the repositories will stay online in read-only mode.

What’s New

The retirement comes down to one core issue: the project became too large and too hard to maintain with a small maintainer team. To keep the ecosystem secure and manageable, the community is directing users toward the Gateway API or alternative Ingress controllers.

Why This is important

This shift impacts anyone running Kubernetes in production. A few key points:

• Reduced long-term support for a widely used controller

• Increased pressure to move toward Gateway API

• Higher security risk if teams continue running an unmaintained controller

• Clearer direction from the community on modern traffic management patterns

My Take

• Ingress NGINX has been a workhorse for years, so seeing it wind down feels big. But the call makes sense.

• If you’re using it in production, start planning your migration now.

• Gateway API brings better extensibility and a cleaner future path, and getting ahead of the transition will save you stress later.

Stay tuned for more updates. I cover cloud news, AWS releases, AI/ML changes, Terraform automation, and the trends shaping DevOps—three times a week, all in one place.

Amazon Web Services (AWS) has added a new capability to Amazon SageMaker Unified Studio you can now create and enforce custom tags for project resources.

What’s New

This feature lets you apply custom tags automatically to all resources created through your SageMaker projects.

If you’re managing large-scale environments, this means stronger consistency and cleaner governance across your organization.

Admins can define project profiles with preset tag configurations, either fixed key-value pairs or default values that can be customized during project creation. Every resource launched from that project inherits those tags automatically.

Why This Matters

This update brings:

• Better alignment with Service Control Policies (SCPs)

• Improved cost tracking across multiple accounts

• Simplified compliance through enforced tag standards

By automating tag management, SageMaker reduces manual errors and helps teams maintain clearer visibility into their resources and spending.

My Take

This is a smart move toward automated tag governance in AWS.
It’s great for teams that need:

• Reliable cost visibility

• Cleaner resource management

• Stronger compliance across environments

Stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

Amazon Web Services (AWS) has launched Amazon EC2 Capacity Manager, a unified dashboard that lets you monitor, analyze, and manage EC2 capacity across all your accounts and Regions.

With this tool, you can view On-Demand, Spot, and Capacity Reservation data in one place. It helps you:

• Visualize usage trends and capacity health

• Identify optimization opportunities

• Automate capacity decisions with smarter insights

Why this matters

This is a big step toward more intelligent and centralized cloud resource management.

Instead of juggling multiple consoles and accounts, engineers can now gain a holistic view of EC2 usage, making planning and cost optimization far easier for multi-account environments.

Stay ahead of the cloud curve. Every week, I share AWS news, feature releases, and the most talked-about DevOps trends, all in one place.

Have you checked out the new AWS Step Functions Metrics Dashboard?

AWS just released a new observability feature for Step Functions, giving you more visibility into your workflow operations: both at the account and state machine levels.

If you’ve been managing distributed applications or data pipelines, this update will make your life easier.

You can now view usage and billing metrics directly in the Step Functions console, all in one place. The dashboard supports both Standard and Express workflows, and it includes familiar metrics like ApproximateOpenMapRunCount.

Everything is now centralized, so you no longer need to hop between CloudWatch or custom dashboards to get a clear picture of your workflow performance.

The new metrics dashboard is available in all AWS Regions where Step Functions is supported.

My take:

• This is a great quality-of-life improvement for DevOps and cloud teams.

• Centralized metrics mean faster insights into performance, cost, and reliability.

• It’s especially helpful for teams running complex workflows who need quick visibility without extra setup.

What do you think? Will this change how you monitor your Step Functions?

Stay ahead of the cloud curve. Every week, I share AWS news, feature releases, and the most talked-about cdevops trends, all in one place.

AWS has quietly released something that could change how developers build on the cloud: the AWS Prompt Library (Beta).

This new tool lets you turn an idea into a production-ready AWS architecture using expert-designed CLI prompts.

No need to start from scratch: simply pick a use case, and the Prompt Library generates configurations that follow AWS best practices.

It currently supports a range of setups: from single web applications to complex microservices architectures.

Why this matters

• Accelerate your cloud builds: go from idea to architecture in minutes.

• Save weeks of planning and configuration.

• Access AWS best practices instantly through prompt-based guidance.

• Focus more on product delivery and scaling.

My Take

I see this as more than just another AWS feature, it’s a shift toward prompt-driven infrastructure. Imagine being able to design your next deployment using natural language prompts instead of hours of documentation and trial setups.

This could be a real productivity boost for startups, small teams, and even enterprise DevOps pipelines.

What do you think?

Would you use AWS’s prompt library to build your next architecture? Reply and tell me how you’d apply it in your workflow , I might feature your response in next week’s edition.

​

Stay ahead of the cloud curve. Every week, I share my insights about AWS news, feature releases, and the most talked-about DevOps trends, all in one place.

What really happened?

AWS just released additional information about the service disruption that happened last week.

Root Cause:

​
A DynamoDB DNS automation bug triggered a rare race condition that accidentally deleted DynamoDB’s endpoint records: breaking connectivity for any AWS service or customer workload that depended on it.

AWS restored full service on October 20 after manual recovery efforts.

• EC2 couldn’t launch new instances for nearly 14 hours

• NLB experienced widespread connection errors

• Lambda, ECS/EKS, Fargate, Redshift, IAM, and Connect all saw degraded performance

AWS Follow-Up Actions:

• Fixing the DynamoDB DNS race condition

• Improving EC2 recovery workflows and throttling mechanisms

• Enhancing NLB failover handling and resilience

What I Learned from This Incident

• Even the most redundant systems can fail when automation goes wrong.

• A small DNS issue can ripple into a full regional outage.

• Human operators remain vital in recovery and resilience.

• Understanding cross-service dependencies is key to designing fault tolerance.

• Reliability is a shared responsibility — across services, teams, and automation layers.

Stay ahead of the cloud curve Every week, I share AWS news, feature releases, and the most talked-about cdevops trends, all in one place.

Here are 9 best practices to help you manage Terraform at scale:

1- Use modules to keep your code DRY, reusable, and easier to manage.

2- Leverage loops and dynamic blocks to reduce repetition and handle different environments more efficiently.

3- Validate variables to catch bad inputs before they cause problems.

4- Manage state remotely with locking enabled for safe team collaboration.

5- Scan for security issues using tools like Checkov, tfsec, or Terrascan.

6- Enforce policies with Open Policy Agent (OPA) to maintain compliance and consistency.

7- Lint your code to catch syntax errors and outdated practices early.

8- Test configurations using Terratest or Kitchen-Terraform before deploying to production.

9- Keep documentation updated with tools like Terraform-docs.

These steps help you keep your infrastructure reliable, secure, and easier to scale.

What’s one Terraform best practice you swear by?

Stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

Here are 4 best practices I follow to keep my Terraform projects clean and scalable:

1. DRY principle

- Don’t repeat yourself.
- Use variables and modules instead of copy-pasting resources.

2. Consistent naming and tagging

- Clear naming and proper tags make management, reporting, and troubleshooting easier.

3. Organized file structure

- Separate files like main.tf, variables.tf, and outputs.tf to keep things clear and manageable.

4. Cost awareness

- Build with budget in mind.
- Use tagging, right-size resources and clean up anything you’re no longer using.

My Takeaway

Clean Terraform code isn’t just about neat files, it’s about creating reliable, scalable infrastructure your team can trust.

What’s one Terraform practice you always follow?

Stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

1️⃣ Verify the security group allows SSH (port 22)
- Check the security group attached to your EC2 instance.
- Ensure there’s an inbound rule allowing traffic on port 22 from your IP address.

2️⃣ Check if the instance has a public IP or Elastic IP
- If the instance is in a public subnet, make sure it has a public IPv4 or an Elastic IP.
- If it's in a private subnet, use a bastion host or VPN to connect.

3️⃣ Ensure you're using the correct key pair
- Verify that you're using the right private key for authentication.
- Ensure the key pair matches the one assigned when the instance was launched.

4️⃣ Use the EC2 Serial Console for debugging
- If SSH isn't working, use the EC2 Serial Console or EC2 Instance Connect (for Amazon Linux 2/Ubuntu).
- Check system logs (/var/log/auth.log or /var/log/secure).

5️⃣ Restart the instance as a last resort
- If none of the above steps work, restart the instance and try connecting again.
- Sometimes, restarting the instance can resolve connectivity issues caused by temporary glitches.

What else would you do?

Wanna be good at AWS? Learn to build, but also learn to fix things when they break.

That’s it for this week. Thanks for reading.

• If you found this useful, like, comment, share and subscribe.

• Have a question or topic you’d like me to cover in a future issue? Hit reply, I’d love to hear from you.

Since then, stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

AWS Step Functions

Think of Step Functions as an orchestration service that lets you build and manage workflows using state machines.

It’s perfect for coordinating multiple AWS services and ensuring tasks run in a specific sequence. You define the steps, and AWS handles the execution, retries, and error handling.


Amazon EventBridge

EventBridge, on the other hand, is an event-driven service that lets you route events between AWS services and third-party applications.

It allows asynchronous, loosely coupled communication between services, making it ideal for real-time event processing and automation.


How to Decide Which One to Choose:

✅ Choose Step Functions if:
- You need to orchestrate complex workflows with multiple steps.
- Your process requires state management, retries, and error handling.
- You’re integrating services that need to follow a specific order
- You want a visual workflow designer to map out execution flows.

✅ Choose EventBridge if:
- You need a fully event-driven architecture with loosely coupled services.
- Your application involves real-time event routing (e.g., triggering Lambda functions when an S3 file is uploaded).
- You’re integrating multiple AWS services and third-party applications via events.
- You want a serverless event bus to simplify communication between systems.

Both services can work together, with EventBridge triggering workflows in Step Functions when certain events occur.

That’s it for this week. Thanks for reading.

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• Have a question or topic you’d like me to cover in a future issue? Hit reply, I’d love to hear from you.

Since then, stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.

AWS Identity and Access Management (IAM)

Think of AWS IAM as the gatekeeper of your AWS resources. It lets you define who can access what in your AWS environment by managing permissions for users, groups, and roles.

AWS Directory Service

AWS Directory Service is more like your corporate phone directory. It provides centralized identity management and authentication for enterprise applications, with options to integrate with your existing Active Directory (AD).

How to Decide Which One to Choose:

Choose AWS IAM if:

• You need fine-grained access control for AWS resources (like S3, Lambda, or EC2).

• You're managing cloud-native applications where directory services aren't necessary.

• You require federated access for external identity providers like Okta or Google Workspace.

Choose AWS Directory Service if:

• You're running enterprise applications that require LDAP or Kerberos authentication.

• You already use Microsoft Active Directory and want to extend it to the AWS cloud.

• You need centralized user and group management for hybrid cloud environments.

Best of Both Worlds:

You can use both AWS IAM and Directory Service together:

• AWS IAM for resource permissions and service-to-service communication.

• AWS Directory Service for centralized authentication and enterprise directory integration.

So, what about you? Have you used IAM, Directory Service, or both in your cloud journey?

That’s it for this week. Thanks for reading.

• If you found this useful, like, comment, share and subscribe.

• Have a question or topic you’d like me to cover in a future issue? Hit reply, I’d love to hear from you.

Since then, stay ahead of the cloud curve. I share AWS news, AI/ML updates, Terraform automation tips, and the biggest DevOps trends, three times a week, all in one place.