DEV Community: Venkata Pavan Vishnu Rachapudi

How Exporting AMIs to S3 Helped Me Recover Customer Resources During a DR Event

Venkata Pavan Vishnu Rachapudi — Tue, 17 Mar 2026 05:36:03 +0000

A few weeks ago, we were dealing with a situation that every cloud engineer eventually faces.

A customer environment needed to be recovered in another region, and we were under time pressure.

At that moment, one of the lesser-known AWS capabilities became incredibly useful.

Exporting AMIs directly to S3.

Using this approach, we were able to:

export the machine image
replicate it to another region
restore it as a new AMI
bring the customer workloads back online

Without needing to share KMS keys or redesign the environment.

That experience reminded me that sometimes the most useful AWS features are the ones people rarely talk about.

Let’s walk through how this works.

What is AMI Store and Restore?

AWS allows you to export an Amazon Machine Image (AMI) to an S3 bucket.

Instead of keeping the AMI only inside EC2 metadata, AWS converts it into a portable binary artifact.

This artifact can then be:

archived
replicated to another region
restored later as a new AMI

Think of it as creating a portable machine image backup.

Architecture Overview

Step 1 — Export the AMI to S3

aws ec2 create-store-image-task \
--image-id ami-xxxx \
--bucket demo-dr-us-east-1-backup \
--region us-east-1

AWS converts the AMI into a compressed .bin artifact.

Example response:

{
 "ObjectKey": "ami-xxxx.bin"
}

Step 2 — Monitor the Export Task

aws ec2 describe-store-image-tasks \
--region us-east-1

Possible states:

InProgress
Completed
Failed

Once completed, the artifact appears in S3.

Step 3 — Verify in S3

aws s3 ls s3://demo-dr-us-east-1-backup

Example output:

ami-0abc123.bin

Step 4 — Replicate to Another Region

Using S3 Cross Region Replication (CRR), the artifact can automatically move to another region.

Primary Region (USA)
        │
        ▼
S3 Bucket
        │
        │ CRR
        ▼
Backup Region (Mumbai)

This ensures the AMI artifact is safely stored in the DR region.

Step 5 — Restore the AMI

From the destination region:

aws ec2 create-restore-image-task \
--bucket demo-dr-ap-south-1-backup \
--object-key ami-xxxx.bin \
--name restored-ami \
--region ap-south-1

This creates a new AMI.

Check for the progess in the destination region AMI Console

Step 6 — Launch the Instance

aws ec2 run-instances \
--image-id ami-new \
--instance-type t3.medium

And the system is restored.

What Happens with KMS Encrypted AMIs?

During export:

AWS internally decrypts the snapshots
packages them into the artifact
restores them in the destination region with new encrypted snapshots

This means you don’t need to share KMS keys across regions, which is often restricted in enterprise environments.

When Should You Use This?

This approach is great for:

enterprise DR strategies
KMS encrypted instance migrations
long term infrastructure backups
cross account migrations
compliance driven archival

Disclaimer: Large AMI Exports May Take Time

When using create-store-image-task, the AMI is exported and stored in Amazon S3 as a binary .bin file.

If the AMI contains large EBS volumes, the resulting artifact can be tens or even hundreds of gigabytes.

In such cases, AWS uploads the image to S3 using multipart upload.

This means:

The export process can take significant time depending on the AMI size.
Multiple parts are uploaded in parallel to improve reliability.
The .bin file will only appear in S3 after the export task is completed.

Because of this, it's important to monitor the store task status before assuming the artifact is available.

You can check the task prsk progress using:

aws ec2 describe-store-image-tasks --region <region>

Final Thoughts

Most AWS engineers never touch store-image-task.

But it’s one of those hidden features that becomes incredibly useful when things go wrong.

In disaster recovery planning, the goal isn’t just backup.

The goal is portability and recovery independence.

Exporting AMIs to S3 gives you exactly that.

Secure Remote Access to AWS Resources from On-Premises

Venkata Pavan Vishnu Rachapudi — Tue, 17 Feb 2026 05:34:51 +0000

Modern AWS architectures avoid exposing networks and instead focus on intent-based access:

Application access
Administrative access
Controlled network access

This reference architecture demonstrates three secure access patterns from on-prem to AWS using:

AWS Client VPN
EC2 Instance Connect Endpoint
AWS Verified Access

Each pattern serves a distinct purpose and should be used together—not interchangeably.

Architecture Context

The VPC contains:

Private EC2 instances
Private RDS databases
Internal Application Load Balancer
No public subnets for compute
No inbound SSH or database ports

Remote users enter AWS only through explicit access services.

Flow 1: Application Access via AWS Verified Access

Use Case

Secure access to private web applications without VPN or public exposure.

Flow

Key Characteristics

Identity-based access
No network-level trust
No VPC-wide visibility
No public ALB required

Flow 2: Administrative EC2 Access via EC2 Instance Connect Endpoint

Use Case

Secure SSH access to private EC2 instances without bastion hosts or public IPs.

Flow

Key Characteristics

IAM-based authentication
Short-lived access
No inbound SSH rules
Fully auditable

Flow 3: Network-Level Access via AWS Client VPN

Use Case

Broad access to private AWS resources such as databases, internal APIs, or legacy tools.

Flow

Key Characteristics

Encrypted tunnel
Route-based access
Subnet-level reachability
Works well for legacy workflows

VPN vs Verified Access (When to Use What)

This is a common design decision, and the wrong choice often leads to overexposure.

Comparison Table

Aspect	AWS Client VPN	AWS Verified Access
Access Model	Network-level	Application-level
Trust Boundary	VPC/Subnet	Identity & policy
User Sees Network	Yes	No
VPN Client Required	Yes	No
Best For	DBs, legacy apps, tools	Web apps, dashboards
Lateral Movement Risk	Higher	Very low
Zero Trust Alignment	Partial	Strong

Simple Rule of Thumb

If users need a network → VPN
If users need an app → Verified Access

Why These Services Work Best Together

Requirement	Service
Internal web applications	AWS Verified Access
EC2 administration	EC2 Instance Connect Endpoint
Database / legacy access	AWS Client VPN

This layered approach ensures:

No over-privileged access
Clear separation of concerns
Reduced blast radius
Easier audits and compliance

Security Best Practices Highlighted

No public EC2 or RDS
No inbound SSH from on-prem
IAM-driven access
Explicit access entry points
Multi-AZ design

Final Thoughts

Secure remote access to AWS is not about choosing one tool.

It’s about:

Matching access method to intent
Avoiding unnecessary network exposure
Enforcing identity at the entry point

By combining:

AWS Verified Access
EC2 Instance Connect Endpoint
AWS Client VPN

you get a secure, scalable, and least-privilege remote access model from on-prem to AWS.

CloudWatch Is No Longer Just for Logs: Inside AWS’s Unified Operational Data Store

Venkata Pavan Vishnu Rachapudi — Tue, 17 Feb 2026 04:59:10 +0000

For a long time, CloudWatch Logs played a very limited role in our cloud operations.

Logs were something we:

Pushed into CloudWatch
Searched during incidents
Forgot about once the issue was resolved

That mental model no longer holds.

AWS has quietly re-architected CloudWatch into something much bigger — a unified operational data store. This change fundamentally alters how CloudOps, Platform, and SRE teams should think about logs on AWS.

This blog breaks down what changed, why AWS did this, and how it impacts real-world cloud operations.

The Big Idea: Logs Are Operational Data

The new CloudWatch Data Sources experience introduces a powerful shift:

Logs are no longer unstructured text — they are operational datasets.

AWS now treats logs the same way data platforms treat analytics data:

Collected centrally
Curated intentionally
Analyzed across multiple tools

This new model is built on three pillars:

Collect → Curate → Analyze

Let’s walk through each.

Collect: Centralized, Org-Level Log Ingestion

Earlier, collecting logs across accounts meant:

Subscription filters
Firehose streams
Custom Lambdas
Per-account maintenance

With the new model, CloudWatch supports organization-level enablement rules.

What this enables

Native ingestion from:
- VPC Flow Logs
- CloudTrail (Org or account scope)
- AWS service logs
Multi-account and multi-region coverage
Single control plane via AWS Organizations

Why this matters

For CloudOps teams managing large AWS estates, this removes a huge amount of undifferentiated work.

🔁 Collect Flow

Curate: Logs Engineering Becomes First-Class

This is the most important — and most overlooked — improvement.

CloudWatch now supports pipelines that allow you to transform and enrich logs during ingestion.

What you can do

Normalize log schemas
Add account, region, environment metadata
Remove noisy or unused fields
Prepare logs for faster queries

Why this is critical

Most log problems are not about volume — they’re about poor structure.

Fixing logs before they land:

Reduces query cost
Improves search performance
Enables consistent analysis across teams

AWS is effectively introducing Logs Engineering as a native CloudOps capability.

🔁 Curate Flow

Analyze: CloudWatch + S3 Tables (The Game Changer)

This is where CloudWatch stops being an ops-only service.

CloudWatch logs can now be materialized into S3 Tables, making them accessible to analytics and ML services.

Supported analysis tools

Amazon Athena
Amazon SageMaker
Amazon QuickSight

Why this is huge

Logs are no longer locked inside CloudWatch
One dataset can serve:
- CloudOps
- Security teams
- Data & analytics teams
No duplicate pipelines or exports

This bridges Cloud Operations and Data Analytics in a clean, AWS-native way.

🔁 Analyze Flow

Real CloudOps Use Cases

This isn’t theoretical — here’s where this actually helps:

Detect abnormal VPC traffic using Athena queries
Share the same logs across AppOps and SecOps
Reduce CloudWatch Logs Insights costs through early curation
Run ML models on historical operational data
Eliminate custom Firehose + Lambda pipelines

This is CloudOps becoming intentional and data-driven.

Example Flow: From VPC Flow Logs to Actionable Insights

You want to:

Collect VPC Flow Logs across all AWS accounts
Normalize and enrich the logs
Analyze traffic anomalies using Athena
Reuse the same data for Security and Ops teams

End-to-End Flow (High Level)

Step-by-Step Breakdown

Collect – Org-Level Log Ingestion

Enable VPC Flow Logs via CloudWatch enablement rules
Logs from all accounts and regions flow into CloudWatch automatically
No Firehose, no Lambda, no per-account setup

CloudOps win: centralized visibility

Curate – Normalize & Enrich Logs

CloudWatch Pipeline performs:

JSON normalization
Adds:

   account_id
   region
   environment

Removes unused fields (cost control)

CloudOps win: query-ready logs, lower cost

Persist – Materialize Logs into S3 Tables

Curated logs are written to S3 Tables
Acts as a long-term operational dataset
Same schema reused everywhere

CloudOps win: one dataset, many consumers

Analyze – Ops & Security Use the Same Data

CloudOps runs Athena queries:

Unusual traffic spikes
Cross-AZ traffic anomalies

SecOps runs:

Suspicious IP analysis
Lateral movement detection
QuickSight dashboards give leadership visibility

CloudOps win: no data duplication, no silos

Incident + RCA Flow

Before:

Logs lived in CloudWatch
Analytics lived elsewhere
Security built separate pipelines
Everyone duplicated effort

Now:

One ingestion
One curated dataset
Multiple use cases

This is CloudOps evolving from reactive troubleshooting to data-driven operations.

Final Thoughts

If you still see CloudWatch as “just a place for logs”, you’re missing the bigger picture.

CloudWatch is becoming:

An operational data platform
A shared foundation for CloudOps, SecOps, and Platform teams
A bridge between real-time monitoring and analytics

And this shift has only just begun.

If you’re building serious AWS platforms, now is the right time to rethink how you treat logs.

Running AEM Without Losing Your Mind: A Practical Local & AWS EC2 Setup Guide

Venkata Pavan Vishnu Rachapudi — Wed, 28 Jan 2026 15:35:14 +0000

I still remember the first time I tried setting up AEM locally — the JAR started, the logs kept scrolling, the fan noise increased, and for a good 10 minutes I wasn’t sure whether AEM was actually starting or silently failing.

If you’ve worked with AEM long enough, you know this feeling.

Adobe Experience Manager isn’t a tool you just install. It’s a platform — built on Java, OSGi, Sling, and a JCR-backed repository — and the learning curve usually starts the moment you try to run it on your machine.

In real projects, getting AEM running reliably (locally and on AWS) is more important than writing your first component. If your setup is unstable, everything else breaks — debugging, component development, deployments, and even confidence.

This guide is based on how I actually set up AEM in day-to-day work — starting locally, understanding what’s happening under the hood, and then moving the same setup to an AWS EC2 instance before thinking about production or AEM as a Cloud Service.

This blog walks you through:

Local AEM setup using Java JAR and Docker
Understanding run modes and debugging
Deploying AEM on AWS EC2
Core AEM consoles: CRXDE Lite, Package Manager, and OSGi System Console

What CRX Actually Is (And Why You’ll Keep Hearing About It)

One of the first things you’ll hear in AEM projects is “check it in CRX” or “look at the node structure”.

CRX stands for Content Repository eXtreme.

Think of CRX as the heart of AEM. Everything — pages, components, dialogs, permissions, configurations — ends up here in some form. Under the hood, modern AEM uses Apache Jackrabbit Oak, which is Adobe’s scalable implementation of the Java Content Repository (JCR) standard.

If you understand CRX at a basic level, debugging AEM becomes much easier. If you don’t, AEM often feels like a black box.

Running AEM Locally Using the Quickstart JAR

This is still the most straightforward way to run AEM — and honestly, the one I recommend when you’re learning or debugging something tricky.

Before You Start

Make sure you have:

Java 11 or 17 (depending on your AEM version)
At least 8 GB RAM on your machine
AEM SDK Quickstart JAR downloaded from Adobe

AEM is heavy. If your laptop struggles here, it will struggle everywhere.

Folder Setup (Keep It Simple)

I always keep author and publish separate. It avoids confusion later.

aem-author/
aem-publish/

Drop the same aem-sdk-quickstart.jar into both folders.

Starting the Author Instance

java -Xmx4g -jar aem-sdk-quickstart.jar -r author,local

The first startup is slow. Don’t panic. Grab coffee.

Once it’s done:

URL: http://localhost:4502
Login: admin / admin

Starting the Publish Instance

After the first run, AEM generates startup scripts under crx-quickstart/bin.

Edit the start script and change the port:

CQ_PORT=4503

Then run with:

java -Xmx4g -jar aem-sdk-quickstart.jar -r publish,local

This separation pays off later when you debug author vs publish issues.

Running AEM with Docker (When You Want Consistency)

Docker makes sense when:

Multiple developers need the same setup
You want to avoid “works on my machine” issues
You’re closer to cloud-style deployments

For pure learning, JAR is fine. For teams, Docker helps.

Basic Folder Layout

aem/
 ├── author/
 │   └── Dockerfile
 ├── publish/
 │   └── Dockerfile
 ├── aem-sdk-quickstart.jar
 └── license.properties

A Simple Author Dockerfile

Keep it readable. Avoid over-optimizing early.

FROM eclipse-temurin:11-jdk

# AEM directories
ENV AEM_HOME=/opt/aem
ENV AEM_RUNMODE=author

# Create AEM home
RUN mkdir -p ${AEM_HOME}
WORKDIR ${AEM_HOME}

# Copy AEM Quickstart and license
COPY aem-author-p4502.jar aem.jar
#COPY license.properties license.properties

# JVM tuning (safe for local testing)
ENV JAVA_OPTS="-Xms1024m -Xmx2048m -XX:+UseG1GC"

# Expose AEM + Debug ports
EXPOSE 4502 58242

# Persist important data
VOLUME [  "/opt/aem/crx-quickstart/repository",  "/opt/aem/crx-quickstart/logs", "/opt/aem/backup"]

# Start AEM Author
CMD java $JAVA_OPTS -jar aem.jar \
  -r ${AEM_RUNMODE} \
  -Dcrx.quickstart.server.port=4502 \
  -Dcrx.quickstart.nodename=author

Build and Run

docker build -t aem-author -f author/Dockerfile .
docker run -d -p 4502:4502 -p 5005:5005 aem-author

Repeat the same idea for Publish on port 4503.

Run Modes: Small Flags, Big Impact

Run modes decide how AEM behaves. Misconfigured run modes are a common reason for “it works locally but not elsewhere” problems.

Common Ones You’ll Actually Use

author – where editors work
publish – where content is served
local – your machine
dev – developer-only configs
nosamplecontent – clean setup

How I Usually Set Them

Java:

-Dsling.run.modes=author,local,dev

Docker:

-r author-local,docker

Avoid dots in Docker run modes — it causes subtle issues.

Verify What’s Active

http://localhost:4502/system/console/status-slingsettings

If run modes are wrong, stop and fix them early.

Debugging AEM (Yes, You Can Debug It Like Any Java App)

AEM debugging feels intimidating until you realize it’s just a Java process.

Enable Debug Port

-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=*:5005

Expose port 5005 if you’re using Docker.

Attach Your IDE

IntelliJ or VS Code
Host: localhost
Port: 5005

This is where AEM starts making sense — Sling Models, services, servlets, all debuggable.

Logs You’ll Check Daily

crx-quickstart/logs/error.log

If something breaks, this file almost always tells you why.

The Three AEM Consoles You’ll Live In

If you’re new to AEM, bookmark these URLs.

CRXDE Lite (`/crx/de`)

Used for:

Inspecting repository structure
Understanding how components are stored
Quick debugging

Rule of thumb: debug here, don’t build here.

Package Manager (`/crx/packmgr`)

This is how code and content move between environments.

Upload packages
Install builds
Deploy Maven outputs

In real projects, almost everything flows through Package Manager.

OSGi System Console (`/system/console`)

This is AEM’s engine room.

You’ll use it to:

Check bundles
Configure services
Troubleshoot weird issues

If AEM feels broken, this is the first place to look.

2. Package Manager (`/crx/packmgr`)

URL:

http://localhost:4502/crx/packmgr

Used for:

Uploading content and code packages
Installing component bundles
Deploying builds from Maven

Typical usage:

mvn clean install -PautoInstallPackage

3. OSGi System Console (`/system/console`)

URL:

http://localhost:4502/system/console

Critical for:

Managing OSGi bundles
Configuring services (/configMgr)
Viewing Sling mappings
Checking logs and components health

Common sub-paths:

/system/console/bundles
/system/console/configMgr
/system/console/components

Running AEM on AWS EC2

When you move AEM from your laptop to EC2, the goal isn’t to make things fancy — it’s to make them predictable.

This setup works without constant memory pressure:

Instance type: t3.medium
RAM: 8 GB
Storage: 30 GB gp3
AMI: Amazon Linux 2023

Anything smaller and AEM startup becomes painful.

Step 1: Create the EC2 Instance

Go to EC2 → Launch instance

Choose Amazon Linux AMI
Select instance type t3.medium
Configure storage → set root volume to 30 GB gp3
Attach an IAM role (explained below)

Configure Security Group:

4502 (Author) – restricted access
4503 (Publish) – restricted access
22 (SSH) – optional
80 / 443 – if needed

🔒 For real environments, never expose 4502/4503 publicly.

Step 2: Access EC2 Without SSH Pain (Recommended)

The cleanest approach is Session Manager.

Attach an IAM role with:

AmazonSSMManagedInstanceCore

This gives you:

Browser-based shell
No SSH keys
No port 22 open
For most use cases, this is enough.

Step 3: Getting AEM SDK onto EC2 (Two Ways)

This is where people usually get confused.

Option 1: SCP Using SSH Keys (Old but Works)

If you already created a key pair:

scp -i my-key.pem aem-sdk-quickstart.jar ec2-user@<EC2-PUBLIC-IP>:/home/ec2-user/

Downsides:

Requires managing SSH keys
Requires port 22 open
Doesn’t scale well

Option 2: Upload to S3 and Pull from EC2 (Best Practice)

This is the cleaner and more cloud-native approach.

Steps:

Upload AEM SDK JAR to a private S3 bucket
Attach an IAM role to EC2 with:
s3:GetObject permission on that bucket
From EC2 (via Session Manager):

aws s3 cp s3://my-aem-artifacts/aem-sdk-quickstart.jar /home/ec2-user/
Why this is better:

No SSH keys
No SCP
Fully auditable
Easy to automate later

This is the approach I recommend.

We can use same commands we used during our local step. For Docker kind of setup we need docker installed on the EC2 Instance

AEM Components Overview

AEM components live under:

/apps/<project>/components

Typical Component Structure

my-component/
 ├── .content.xml
 ├── _cq_dialog/
 │   └── .content.xml
 ├── my-component.html

Components are deployed as:

Maven-built OSGi bundles
Content packages via Package Manager

AEM Setup Pyramid

Below is how I personally visualize AEM setup and learning progression — from infrastructure to development and operations.

Practical AEM Flow (How I Recommend Working)

Final Words

If you understand:

How CRX (Content Repository eXtreme) works
Why Author ≠ Publish
How run modes control behavior
How /crx/de, /crx/packmgr, and /system/console fit together

👉 You’re already ahead of most beginners in AEM.

Master the fundamentals locally, automate everything with Maven, and only then think about scaling on AWS or Cloud Service.

Happy AEM engineering 🚀

Getting Started with AEM: On-Prem vs AEM Cloud (In Simple Terms)

Venkata Pavan Vishnu Rachapudi — Sat, 17 Jan 2026 12:16:56 +0000

If you’ve ever worked in large enterprises, you’ve probably heard people say:

“Our website runs on AEM.”

But what exactly is AEM?
And what’s the difference between AEM On-Prem and AEM as a Cloud Service?

Let’s break it down — no heavy jargon, just how things actually work.

So, What is AEM?

Adobe Experience Manager (AEM) is a platform that helps companies:

Build websites
Manage content
Create forms
Store and reuse digital assets
Deliver consistent experiences across channels

Think of AEM as:

A powerful content engine that sits between business users and developers.

Where Do Companies Use AEM?

AEM is usually chosen when:

Websites are large and complex
Multiple teams update content
Performance and security really matter
Content needs to be reused across brands or regions

You’ll commonly see AEM in:

Banking & Finance
Retail
Healthcare
Media companies
Government portals

AEM On-Prem vs AEM Cloud – Let’s Talk Reality

AEM On-Prem (Traditional Way)

With On-Prem, you own almost everything:

Servers
Scaling
Patching
Upgrades
Availability

It gives control, but also responsibility.

AEM Cloud (Modern Way)

With AEM as a Cloud Service:

Adobe manages infrastructure
Scaling happens automatically
Updates are continuous
CI/CD is built in

You focus on content and code, not servers.

High-Level Comparison (Quick View)

Core AEM Building Blocks (Common Everywhere)

No matter where AEM runs, the core ideas stay the same.

AEM Sites – Where Pages Are Built

AEM Sites is what marketing teams live in.

It allows:

Drag-and-drop components
Editable templates
Multi-language support
Multi-site management

In simple terms:

Developers build components.
Authors assemble pages.

AEM Forms – When Data Matters

AEM Forms is used when you need more than just content.

Typical examples:

Bank application forms
Insurance claims
Government submissions

It supports:

Adaptive forms
Validation
Workflows
Secure data handling

AEM Content & Assets – One Central Content Hub

AEM isn’t just pages.
It’s also about content reuse.

With AEM Assets (DAM):

Images, videos, PDFs live in one place
Metadata & tagging help discovery
Assets can be reused across sites

Apache Sling – How URLs Talk to Content

This is where AEM gets interesting.

Apache Sling maps URLs directly to content stored in AEM.

Instead of thinking:

URL → Controller → View

AEM thinks:

URL → Content → Script

Jackrabbit JCR – The Content Brain

Behind the scenes, AEM stores everything in a JCR (Java Content Repository).

What goes into JCR?

Pages
Components
Templates
Configurations
User permissions

It’s not a traditional database.

Think of it as:

A tree-structured content store optimized for content, not tables.

    Root --> Content --> Site --> Page --> Component

Apache Felix – Why AEM Is Modular

AEM runs on Apache Felix (OSGi).

This means:

Everything is a bundle
Services can be updated independently
No full server restart needed

Developers love this because:

You deploy features, not monoliths.

On-Prem vs Cloud – Operational View

So… Which One Should You Choose?

Choose AEM On-Prem if:
- You need full infra control
- You have legacy dependencies
- Compliance restricts cloud usage
Choose AEM Cloud if:
- You want faster releases
- You prefer less operational work
- You’re building modern digital experiences

Final Thoughts

AEM is not just a CMS.

It’s a content platform powered by:

Apache Sling
JCR
OSGi
Enterprise workflows

And while On-Prem still exists, AEM Cloud is clearly the future.

🩺 How I Troubleshoot an EC2 Instance in the Real World (Using Instance Diagnostics)

Venkata Pavan Vishnu Rachapudi — Mon, 12 Jan 2026 16:58:10 +0000

When an EC2 instance starts misbehaving, my first reaction is not to SSH into it or reboot it. Instead, I open the EC2 console and go straight to Instance Diagnostics.

Over time, I’ve realized that most EC2 issues can be understood — and often solved — just by carefully reading what AWS already shows on this page.

In this blog, I’ll explain how I use each section of Instance Diagnostics to troubleshoot EC2 issues in a practical, real-world way.

The First Question I Answer

Before touching anything, I ask myself one simple question:

Is this an AWS infrastructure issue, or is it something inside my instance?

Instance Diagnostics helps answer this in seconds.

Status Overview: Always the Starting Point

I always begin with the Status Overview at the top.

Instance State

This confirms whether the instance is running, stopped, or terminated.

If it is not running, there is usually nothing to troubleshoot.

System Status Check

This reflects the health of the underlying AWS infrastructure such as the physical host and networking.

If this check fails, the issue is on the AWS side. In most cases, stopping and starting the instance resolves it by moving the instance to a healthy host.

Instance Status Check

This check represents the health of the operating system and internal networking.

If this fails, the problem is inside the instance — typically related to OS boot issues, kernel problems, firewall rules, or resource exhaustion.

EBS Status Check

This confirms the health of the attached EBS volumes.

If this fails, disk or storage-level issues are likely, and data protection becomes the immediate priority.

CloudTrail Events: Tracking Configuration Changes

If an issue appears suddenly, the CloudTrail Events tab is where I go next.

I use it to confirm:

Whether the instance was stopped, started, or rebooted
If security groups or network settings were modified
Whether IAM roles or instance profiles were changed
If volumes were attached or detached

This helps quickly identify human or automation-driven changes.

SSM Command History: Understanding What Ran on the Instance

The SSM Command History tab shows all Systems Manager Run Commands executed on the instance.

This is especially useful for identifying:

Patch jobs
Maintenance scripts
Automated remediations
Configuration changes

If there are no recent commands, that information itself is useful because it confirms that no SSM-driven actions caused the issue.

Reachability Analyzer: When the Issue Is Network-Related

If the instance is running but not reachable, I open the Reachability Analyzer directly from Instance Diagnostics.

This is my go-to tool for diagnosing:

Security group issues
Network ACL misconfigurations
Route table problems
Internet gateway or NAT gateway connectivity
VPC-to-VPC or on-prem connectivity issues

Instead of guessing, Reachability Analyzer visually shows exactly where the network path is blocked.

Instance Events: Checking AWS-Initiated Actions

The Instance Events tab tells me if AWS has scheduled or performed any actions on the instance.

This includes:

Scheduled maintenance
Host retirement
Instance reboot notifications

If an issue aligns with one of these events, the root cause becomes immediately clear.

Instance Screenshot: When the OS Is Stuck

If I cannot connect to the instance at all, I check the Instance Screenshot.

This is especially helpful for:

Identifying boot failures
Detecting kernel panic messages
Seeing whether the OS is stuck during startup

Even a single screenshot can explain hours of troubleshooting.

System Log: Understanding Boot and Kernel Issues

The System Log provides low-level OS and kernel messages.

I rely on it when:

The instance fails to boot properly
Services fail during startup
Kernel or file system errors are suspected

This is one of the best tools for diagnosing OS-level failures without logging in.

[[0;32m  OK  [0m] Reached target [0;1;39mTimer Units[0m.
[[0;32m  OK  [0m] Started [0;1;39mUser Login Management[0m.
[[0;32m  OK  [0m] Started [0;1;39mUnattended Upgrades Shutdown[0m.
[[0;32m  OK  [0m] Started [0;1;39mHostname Service[0m.
         Starting [0;1;39mAuthorization Manager[0m...
[[0;32m  OK  [0m] Started [0;1;39mAuthorization Manager[0m.
[[0;32m  OK  [0m] Started [0;1;39mThe PHP 8.2 FastCGI Process Manager[0m.
[[0;32m  OK  [0m] Finished [0;1;39mEC2 Instance Connect Host Key Harvesting[0m.
         Starting [0;1;39mOpenBSD Secure Shell server[0m...
[[0;32m  OK  [0m] Started [0;1;39mOpenBSD Secure Shell server[0m.
[[0;32m  OK  [0m] Started [0;1;39mDispatcher daemon for systemd-networkd[0m.
[[0;1;31mFAILED[0m] Failed to start [0;1;39mPostfix Ma… Transport Agent (instance -)[0m.
See 'systemctl status postfix@-.service' for details.
[[0;32m  OK  [0m] Started [0;1;39mLSB: AWS CodeDeploy Host Agent[0m.
[[0;32m  OK  [0m] Started [0;1;39mVarnish HTTP accelerator log daemon[0m.
[[0;32m  OK  [0m] Started [0;1;39mSnap Daemon[0m.
         Starting [0;1;39mTime & Date Service[0m...
[   13.865473] cloud-init[1136]: Cloud-init v. 25.1.4-0ubuntu0~22.04.1 running 'modules:config' at Fri, 05 Dec 2025 01:25:29 +0000. Up 13.71 seconds.

Ubuntu 22.04.3 LTS ip-***** ttyS0

ip-****** login: [   15.070290] cloud-init[1152]: Cloud-init v. 25.1.4-0ubuntu0~22.04.1 running 'modules:final' at Fri, 05 Dec 2025 01:25:30 +0000. Up 14.98 seconds.
2025/12/05 01:25:30Z: Amazon SSM Agent v3.3.2299.0 is running
2025/12/05 01:25:30Z: OsProductName: Ubuntu
2025/12/05 01:25:30Z: OsVersion: 22.04
[   15.189197] cloud-init[1152]: Cloud-init v. 25.1.4-0ubuntu0~22.04.1 finished at Fri, 05 Dec 2025 01:25:30 +0000. Datasource DataSourceEc2Local.  Up 15.16 seconds
2025/12/15 21:35:50Z: Amazon SSM Agent v3.3.3050.0 is running
2025/12/15 21:35:50Z: OsProductName: Ubuntu
2025/12/15 21:35:50Z: OsVersion: 22.04
[1091674.876805] Out of memory: Killed process 465 (java) total-vm:11360104kB, anon-rss:1200164kB, file-rss:3072kB, shmem-rss:0kB, UID:1004 pgtables:2760kB oom_score_adj:0
[1091770.835233] Out of memory: Killed process 349683 (php) total-vm:563380kB, anon-rss:430132kB, file-rss:4096kB, shmem-rss:0kB, UID:0 pgtables:1068kB oom_score_adj:0
[1092018.639252] Out of memory: Killed process 347300 (php-fpm8.2) total-vm:531624kB, anon-rss:193648kB, file-rss:3456kB, shmem-rss:106240kB, UID:33 pgtables:888kB oom_score_adj:0

Session Manager: Secure Access Without SSH

If Systems Manager is enabled, I prefer using Session Manager to access the instance.

This allows me to:

Inspect CPU, memory, and disk usage
Restart services safely
Avoid opening SSH ports or managing key pairs

From both a security and operational standpoint, this is my preferred access method.

What Experience Has Taught Me

Troubleshooting EC2 instances is not about reacting quickly — it is about observing carefully.

Instance Diagnostics already provides:

Health signals
Change history
Network analysis
OS-level visibility

When used correctly, these tools eliminate guesswork and reduce downtime.

Final Thoughts

My approach to EC2 troubleshooting is simple:

Start with Instance Diagnostics.

Understand the signals.

Act only after the root cause is clear.

In most cases, the answer is already visible — we just need to slow down and read it.

☁️ What If I Move to the Cloud? Part 1 – What Is This Cloud, Really?

Venkata Pavan Vishnu Rachapudi — Sat, 10 Jan 2026 15:28:00 +0000

Hello friends! 🙏 Welcome to the first post in my series: “What If I Move to the Cloud?”

So, your manager comes and says: “We need to move everything to the cloud this year.” And you’re sitting there like Bahubali, sword in hand ⚔️, thinking: “But… where is this cloud? Who am I fighting?” 😅

Don’t worry yaar, I’ll explain. Let’s make it simple, funny, and a little cinematic. 🎬

🌥️ So… What Exactly Is the Cloud?

First of all, cloud is not some floating thing above the sky ☁️. It’s not going to rain servers on your head. 😄

The cloud is basically computing resources that live somewhere else, which you can use on demand. Someone else manages them, you just use them. Like your friend who always brings vada pav to your party—you enjoy, he does the work! 🍔

Think of it like this:

Before cloud: You have your own little shop 🏠—servers, storage, network. You handle everything yourself: buying hardware, installing, patching, keeping it running 24/7.
After cloud: You rent a shop 🏢 that is fully managed. You bring your ingredients (apps, data), cook, serve, enjoy. The landlord (cloud provider) handles electricity, plumbing, security, cleaning—even AC! ❄️

💡 Before vs After Cloud (Infographic)

Some generic examples:

Scenario	Before Cloud	After Cloud
Email	Host your own email server 🖥️, patch it, handle spam & downtime	Use Gmail or Outlook 365 📧, scale storage automatically
Website	Deploy on office server 💻, upgrade RAM manually, crash on traffic spikes	Deploy on cloud 🌐, auto-scale during peaks like IPL or movie release day 🚀
Storage	USB drives, NAS, tapes for backups 💾, manage RAID & power	Upload to S3 or Drive ☁️, access anywhere, versioning
Data Analysis	Run Excel on desktop 📊, slow, memory-limited	Use Databricks / Snowflake 🧮, big datasets, fast queries

🍫 Cloud is Like Chocolate (With Flavors!)

Now, friends, cloud is chocolate 🍫

All clouds do the same basic thing: compute, storage, networking, scaling
But they have different flavors:
- AWS → Milk chocolate 🥛
- Azure → Dark chocolate 🌑
- GCP → Hazelnut chocolate 🌰

Different taste, same sweet satisfaction.

Example: Load balancing

AWS → Elastic Load Balancer
Azure → Azure Load Balancer
GCP → Cloud Load Balancing

Different setup, same idea: scale servers automatically when traffic spikes.

🤩 Why Move to the Cloud?

Cloud is sweet, let me tell you why:

💰 Pay-As-You-Go

Only pay for what you use. Like buying paav bhaji only for hunger instead of hoarding 10 packets 🥘.

Example: Netflix 💻 doesn’t run huge servers 24/7. During off-peak, servers chill. During IPL or weekend binge-time, servers go full Bahubali mode ⚔️. Only pay for what you consume.

📈 Scale on Demand

Traffic spikes happen. Lunch rush at Swiggy 🍔, dinner rush at Zomato 🍕, or PK suddenly going viral in 200 countries 🌎.

Cloud scales automatically—more compute when you need it, less when traffic calms.

⚡ Faster Experimentation

Cloud is like PK exploring Earth 🤯. Try new things, test, fail, retry—no hardware worries.

Example: Adobe Creative Cloud ✨ launches new AI features quickly, tests globally, rolls back if something breaks.

🛠️ Less Operational Headache

No babysitting servers, no patching, no power cuts. Focus on apps, features, customer delight.

Example: Netflix doesn’t hire people to watch servers like Baahubali guarding the fortress. They focus on streaming and original content.

🌍 Global Reach

Serve users anywhere, anytime. Zomato delivers in Delhi, Dubai, and maybe Dwaraka too 😉. Adobe serves creatives worldwide without latency nightmares.

⚠️ The Dark Side of Cloud 🍫⚠️

Not everything is sweet chocolate. Some bites are bitter:

💸 Cost surprises: Startups run hundreds of VMs for testing, bill shocks later
📜 Data rules: Some data can’t leave the country, compliance matters
⚙️ Legacy headaches: Old monolith apps may crash or lag if lifted blindly
⛓️ Vendor lock-in: Too cozy with one cloud makes future switching painful

🌐 Hybrid Cloud – Sweet & Bitter Balance

Because of these challenges, hybrid cloud emerged:

Sensitive workloads stay on-prem 🏢
Scalable, bursty workloads move to cloud ☁️

Example: Banks keep core transactions on-prem 💳 but fraud detection AI runs in cloud ☁️.

Hybrid cloud = milk chocolate with a dark chocolate filling 🍫—sweet, but keeps bitter controlled.

✅ Key Takeaways:

Cloud = Pay for what you use, scale automatically, experiment fast, focus on apps, reach globally
But beware: cost surprises, data rules, legacy apps, vendor lock-in
Hybrid cloud = compromise solution, balance sweet & bitter

Next in the series: Total Cost of Ownership (TCO)—how to calculate if cloud saves money or just creates bills 💸

How I Became an AWS Community Builder (Security) – My Real Journey + Application Guide

Venkata Pavan Vishnu Rachapudi — Thu, 25 Dec 2025 18:08:16 +0000

Hey folks! 👋

If you've ever wanted to level up your AWS game, meet amazing people, and get $500 AWS credits + free cert vouchers, then the AWS Community Builders program is calling your name.

I joined last year in Security and these 11 months have been 🔥. Here's my real story + exact steps to apply.

What Even is AWS Community Builders? 🤔

It's for passionate AWS users who share knowledge – bloggers, speakers, creators like us.

You get:

Mentorship from AWS pros
Exclusive resources
Global networking
Real perks (more on that later)

Choose where you've actually contributed!

My Pre-Community Builder Grind 💪

I wrote simple, practical blogs (not complex architectures):

✅ GuardDuty + Inspector with EC2/ECR
✅ Secure EC2 access (NO port 22!)
- EC2 Instance Connect Prefix Lists
- Bastion Hosts
- Systems Manager (w/ session logs)
✅ Nginx/Tomcat on EC2 (Console/SDK/CLI/CF IAAC)

Pro tip: Beginners love these. Show 4 deployment ways + security basics = instant value.

Also crushed AWS Amplify in 30 Days Hackathon by AWS User Group India 🏆

Community Builder Life Changed Everything 🚀

Spoke at 3 events

Met legends (can't name-drop, but WOW)
Deep dived Security + AI
Got real feedback that leveled me up

The people? Pure gold. That's the real magic.

The Perks (Swag Lovers Rejoice! 🎁)

💰 $500 AWS Credits – POCs galore!
📜 100% Free Cert Voucher
📦 Swag (random blog rewards – I'm obsessed)

🎓 *Exclusive QA Cloud Acadmey Education Platform Access *

How to Actually Get In (Step-by-Step) 📋

1. Build Your Portfolio (8-10 quality pieces)

✅ Original blogs (your category)
✅ Attend Events of AWS User Groups and Contribute to it

✅ Talks/videos/social
✅ NO plagiarism – be real

2. Application Process

📅 Opens mid-January (global)
🔗 Join waitlist NOW: https://pulse.aws/application/BM2AKLSX?p=0
📎 Submit links + your passion story

3. My Tips (That Actually Work)

✅ Start NOW – consistency wins
✅ Pick YOUR niche (not trends)
✅ Engage – attend events, comment, collab
✅ Not selected? Reapply! (2nd/3rd time common)
✅ You don't need "expert" – just eager to learn/help

Resources to Crush It Today 🌐

The Real Goal 🎯

Contribute to AWS ecosystem. Builder or not, keep sharing. That's what grows you + helps others .

Mid-January apps opening soon – start your portfolio TODAY!

You in? Drop a 🔥 if you're applying!

Connect with me, share your AWS journey, let's grow together 🚀

From Panelist & Mentor to Speaker to AWS Certified – A Defining Week in My AWS Journey

Venkata Pavan Vishnu Rachapudi — Thu, 25 Dec 2025 06:45:22 +0000

Some weeks don’t just fill up your calendar — they change your direction.

For me, December 18th to 23rd was exactly that kind of week: three different roles, one common thread — helping people really understand how cloud and AI fit together in the real world.

🎯 One week, three hats

In six days, I got to wear three hats:

Panelist & mentor at an AWS Student Community Day in Hyderabad.
Speaker at AWS Community Day Kochi (AI/ML Edition).
Newly certified AWS Generative AI Developer – Professional.

Individually, each one is special.

Together, they felt like a mini “sprint” in my AWS journey — community, practice, and validation all reinforcing each other.

🎓 Dec 18 – Panelist & mentor at AWS Student Community Day (MLRIT, Hyderabad)

The week kicked off at MLRIT in Hyderabad with AWS Student Community Day, where I joined as a panelist and mentor.
Walking into a hall full of students always reminds me of my own early days — lots of curiosity, a bit of anxiety, and a ton of questions that don’t always get asked out loud.

Real talk on careers, not buzzwords

On the panel, we kept the conversation honest and practical:

What does a realistic cloud + AI career path look like for students today?
How do you move from “I passed a cert” to “I can actually solve problems”?
What do hiring teams expect from freshers beyond a list of technologies?

Instead of throwing jargon, we shared stories — projects that broke, lessons from production, and how community involvement can shortcut years of trial-and-error.

Hands-on: building instead of just listening

After the panel, I switched into mentor mode for a hands-on lab.
The goal was simple: ship something end-to-end, not just click around a console.

We worked through:

Evolution of Coding and Kiro Features
Vibe coding with Kiro.dev to quickly get from idea to working UI.
Deploying with AWS App Runner so students could see their app live, not just on localhost.
Thinking about scalability, availability, and security as part of the design, not as an afterthought.

By the end, the best feedback wasn’t “nice session” — it was “this finally makes sense now.”

That shift from “cloud feels abstract” to “oh, I can actually build this” is exactly why these workshops matter.

🎤 Dec 20 – Speaker at AWS Community Day Kochi (AI/ML Edition)

Two days later, the vibe changed completely: from campus energy to community conference energy at AWS Community Day Kochi.
This time I was on stage, talking about something that’s very close to what I work on day to day: Agentic AI and how enterprises are starting to adopt it.

From prompts to Agentic AI

The session was built around three ideas:

Where Amazon QuickSuite and AWS Transformfit in
- Amazon QuickSuite as a day-to-day AI companion for research, chat, automation, and flows — almost like a teammate that understands your systems.
- AWS Transform as the heavy-duty engine for modernization: from VMware and mainframes to .NET migration, powered by Agentic AI.
Real patterns, not just slides

We walked through patterns like automating content workflows, migration of boto2 SDK to Boto3 SDK using Transform instead of “cool demo only.”

What stood out to me was the Q&A afterwards — people weren’t asking “What is Agentic AI?” anymore; they were asking “How do I plug this into my environment?”

That’s when you know the conversation has moved from hype to actual adoption.

📘 Dec 23 – AWS Certified Generative AI Developer – Professional

The week wrapped up on a personal high: earning the Beta AWS Certified Generative AI Developer – Professional certification.

This exam goes well beyond “call an API and get a response.”

It tests whether you can design secure, scalable GenAI systems that live comfortably inside real-world AWS architectures.

Some of the deeper areas it touches:

Amazon Bedrock architecture and security — not just “what is it,” but how should you use it.
Designing agent-based, multi-step workflows that coordinate tools, APIs, and services.
Using Lambda, API Gateway, and Step Functions to glue GenAI components into full pipelines.

While answering questions, there were multiple moments of “wait, I literally spoke about this on stage two days ago.” and "heard about from fellow speaker "
That overlap between community work and exam content made the certification feel less like a separate goal and more like a checkpoint on the same path.

🔁 What this week taught me

Looking back, going from panelist & mentor → speaker → certified professional in a single week was intense, but it reinforced a few beliefs that guide how I want to continue showing up in the AWS community.

Teaching sharpens understanding

Every time you explain something, you find edge cases and blind spots you hadn’t noticed before. Students and community members ask the questions documentation doesn’t.
Community is a multiplier

Panels, meetups, and community days compress learning. You don’t just learn from talks — you learn from hallway conversations, random questions, and even “we tried this and it failed” stories.
Certifications hit different with real context

When you’ve built, broken, and fixed real systems, an exam doesn’t feel like memorization. It feels like someone asking, “Okay, show me how you’d do this in the real world.”

Most of all, this week reminded me that giving back and growing personally are not separate tracks.

The more you mentor, speak, and share, the deeper your own understanding becomes.

🙏 Thank you — and what’s next

None of this happened in isolation.

Huge thanks to:

AWS Student Community & AWS Cloud Clubs at MLRIT for creating a space where students can experiment, break things, and learn by doing.
The AWS Community Day Kochi organizers and volunteers for building a stage where practitioners can talk about what’s actually happening with AI/ML on AWS.
SUDO Consultants for backing community work and giving me the room to build, experiment, and share.
Every student and attendee who showed up, asked tough questions, and shared honest feedback — you made the week memorable.

From here, the plan is simple: keep building, keep teaching, and keep pushing deeper into Agentic AI and cloud automation.

If you’re on a similar path — maybe just getting started with AWS, or trying to move from “I know the services” to “I can design systems” — consider this an open invite: join a community, share what you learn, and let your journey be shaped by contribution as much as personal milestones.

AWS App Runner vs ECS Express Mode: Git, ECR, Scaling, and Security Explained

Venkata Pavan Vishnu Rachapudi — Wed, 24 Dec 2025 15:13:28 +0000

Introduction

AWS provides modern ways to run containerized applications without managing servers. Two popular options are AWS App Runner and Amazon ECS Express Mode. Both simplify container deployments, but they target different levels of control and use cases.

This blog compares these services and provides guidance on which to use for different workloads, based on AWS documentation.

Service Overview

AWS App Runner

App Runner is a fully managed service for running web apps and APIs:

Git Integration: Connect GitHub, GitLab, or Bitbucket directly to deploy applications.
Container Registry Support: Deploy pre-built container images from Amazon ECR (public or private) or Docker Hub.
Auto-scaling: Concurrency-based scaling; configure min/max instances and max requests per instance.
Security: Built-in HTTPS, AWS-managed TLS certificates, and WAF support.
Logging & Monitoring: CloudWatch logs and metrics out-of-the-box.
Public/Private Access: Default public endpoint; can connect to VPC for private resources.
Use Case: Stateless web apps, public APIs, rapid deployment with minimal operational overhead.

Amazon ECS Express Mode

ECS Express Mode is a simplified ECS deployment option:

ECS Power: Runs on ECS + Fargate without manual cluster management.
Networking & Security: Full VPC, IAM roles, subnets, security groups.
Scaling: CPU, memory, or custom metric-based auto scaling.
Workloads: Supports sidecars, background jobs, and complex internal services.
CI/CD: Requires building container images and pushing to ECR or another registry.
Use Case: Enterprise workloads, private/internal services, and production applications needing full control.

Feature Comparison Table

Feature	App Runner	ECS Express Mode
Git Integration	✅ Yes	❌ No
Container Registry	✅ ECR	✅ ECR
Auto Scaling	✅ Concurrency-based	✅ CPU/memory/custom metrics
WAF Support	✅ Yes	✅ via ALB
HTTPS	✅ Built-in	✅ ALB + ACM
Public Endpoint	✅ Yes	✅ Yes
Private Networking	⚠️ Limited (VPC connector)	✅ Full
Logging & Metrics	✅ CloudWatch	✅ CloudWatch
Background Workers / Sidecars	❌	✅ Yes

Go-To Scenarios

App Runner:
- Fast prototyping
- Public-facing web apps or APIs
- Minimal infra management
- Deploy from Git or ECR
ECS Express Mode:
- Enterprise workloads
- Private/internal services
- Metric-driven auto scaling
- Complex container architectures
- Deploy from container registry only

Expert Tip: Start with App Runner for MVPs or demos. Move to ECS Express for production workloads requiring security, VPC, and custom scaling.

Conclusion

AWS App Runner provides application-level, concurrency-based auto scaling with built-in Git/ECR integration, WAF, HTTPS, and logging, ideal for quick deployments.

ECS Express Mode provides infrastructure-level control, advanced networking, and flexible scaling, making it suitable for enterprise-grade workloads.

Decision: Use App Runner for speed and simplicity, ECS Express Mode for control and production readiness.

References

Stop the EBS Madness: Automate Your AWS Storage Savings NOW

Venkata Pavan Vishnu Rachapudi — Sun, 23 Nov 2025 11:54:07 +0000

Ever had that moment at 2 AM when your AWS bill is sky-high just because you forgot about some EBS volumes you created last quarter? Yeah, us too. It’s like cloud storage is secretly binge-eating your budget while you sleep.

Well, brace yourself—AWS Compute Optimizer just dropped a feature that lets you automate EBS volume cleanups and upgrades so you can finally take back control (and cash)!

Why Are EBS Volumes Such Budget Bandits?

You deploy, test, and migrate. Suddenly, your account is littered with unused volumes.
Upgrades? Who has time for that? You're running old gp2 volumes because “it works, right?”
Manual cleanup is a soul-sucking ritual. Don’t let yourself be THAT engineer.

Plot Twist: AWS Automation to the Rescue!

this new Compute Optimizer feature means:

Set up daily, weekly, or monthly rules.
Pick a time when nobody is awake—say, midnight to 1 AM.
Automation runs silently, like a ninja cleaning up your cloud mess.

Rule the Rules (Literally)

Want total control? Create org-wide rules or just target specific accounts. Decide if automation runs before or after other member rules.

It’s flexible, just like your sleep schedule!

Pick Your Weapons: Action Types

Snapshots before deleting a volume? Of course. Data safety first!
Upgrading stale gp2 volumes to snazzy gp3 or io2? One-liner.

Share Only with Those Who Matter

Fine-tune automation:

Target regions where zombie volumes lurk.
Filter by tags—finally see “dev” and “prod” labels earn their keep.
Preview estimated monthly savings. (Spoiler: it's satisfying.)

Check Your Scoreboard: Automation Dashboard

Watch your savings climb and track every automated action:

Rollbacks, failures, and jaw-dropping savings display in real time.

Most Satisfying Button Ever: Activate/Deactivate

Turn your rule on/off at will. Want to test? Go inactive. Ready to brag about savings? Go active. It’s that easy.

Pro Tips to Make It Rock

Start in dev/stage, not prod.
Review snapshots—AWS auto-protects you, but an extra pair of eyes never hurts.
Set notifications so you’re first to know any action.
Use tags—seriously, they’re a lifesaver on big clouds.

Zen Mode: Warnings and Nuggets

Creating EBS snapshots can take hours—patience, padawan.
Wait at least six hours after a volume is touched before touching it again.
Up to 100 actions run in parallel—you’re officially managing a cloud army.
Savings update live. Screenshot the dashboard for team meeting bragging rights!

The Ultimate Showdown: You vs. Zombie Volumes

Stop letting AWS EBS volumes run wild in your account. Reign them in with Compute Optimizer, automate the boring stuff, and pocket the savings you deserve.

This release is fresh, powerful, and (dare we say) fun—so get in, experiment, and see how much you can save.

Hot take: If you’re still doing EBS cleanup by hand, you’re working too hard.

Let the robots do it and put your brain back where it belongs—in the innovation game.

Evolution of Agentic AI C/O Amazon Quicksuite

Venkata Pavan Vishnu Rachapudi — Sat, 22 Nov 2025 12:19:56 +0000

Today, whatever is new quickly becomes old. We started with AI, then moved to Generative AI, and now it’s Agentic AI. Honestly, the lines blur because everything overlaps and shines depending on our use cases and requirements.

Before diving deeper, it’s also key to clarify the difference between Generative AI and Agentic AI.

Generative AI is reactive; it creates content—text, images, code—based on user prompts. It focuses on what to create when asked.
In contrast, Agentic AI is proactive and autonomous. It takes initiative, sets goals, plans multi-step workflows, makes decisions, adapts dynamically, and executes tasks with minimum supervision.
Generative AI powers content within these systems, but Agentic AI orchestrates entire processes to achieve goals efficiently, turning AI from a passive tool into an active partner driving outcomes.

This post gives you a glimpse of the newest addition to AWS’s agentic AI stack—Amazon QuickSuite.

The name says it all:

Quick — enabling you to build agent flows, create agentic AIs, conduct deep research, dive into your data, or even build your own personal chat agent like your own GPT—all really fast, right at your fingertips.
Suite — because it’s a family of tools: Quick Flow, Quick Automate, Quick Agents, Quick Research, and more.

QuickSuite is an agentic AI ecosystem delivered as a SaaS offering from AWS. Before this, building agentic AIs with Bedrock agents meant you had to manage model invocations, quotas, Lambda runtimes, observability, security, and more. Now, all that complexity is gone.

My QuickSuite Use Cases

Automated content generation for sales and marketing
AWS assistant for Weekly Updates
Resume analyzer with Quick Automate

Quick Suite Dashboard

Quick Flows

It is a no-code/low-code automation feature that lets users create intelligent workflows using natural language prompts.
It automates repetitive or routine tasks by turning simple descriptions into fully functioning workflows, connecting data and actions seamlessly across apps without coding

Automated content generation for sales and marketing

You can how fast i created an agentic ai which handles multiple tasks as below. with just bare minimum prompting thats all .
We can also edit it , by going into editor mode and also we can text fields, file upload fields , integrations , UI Agents ,etc.

Final Flow Output:

AWS assistant for Weekly Updates

Mode of Searches are three types one is General Knowledge uses Gen AI , Web search does web browsing and last one is quicksuite data which is skimming through your enterprise data

After running my flow here is the output

Resume Analyser Agent

Create me a resume analyser where Users uploades files which must be pdf,docx,txt which must 5  files at time.
please make sure i can upload 3 files at a time if needed add reasoning flow to that 

user will enter information like for which role , experince , 
then provide me recommdations , certifications , weak strong , compare between other profiles resumes .
genreate final info

we can still improvise it very well enough and add other functionality too.

Quick Automations

Creates multi-agent automations for business processes
Automate end-to-end enterprise processes with ease. Build, test, and deploy sophisticated automations using natural language or documentation.

AI Footprint Analyst

simply created from the prompting

create be AI Foot print analyst AI which goes to UI agent Web browsing and checks information for and also it will checks latest cloud providers updates in the AI

On the left side you can see we can drag a lot Action components like unzip folders , pdf text extraction , excel data extraction , UI Agent , Python code block , process flows , Data Tables ( We can perform CRUD) ,

Chat Agents

Build personalized AI chat assistants capable of multiple integrated tasks.

Extensions

Quick Suite supports web browser extensions to firefox , chrome and microsoft edge

Then download and add the Amazon QuickSuite Browser extension

Let summarize AWS IVS Service using the quicksuite browser extension

we can also upload our local files we can also control on which tab we need to have our extension enabled.

Integrations

Quick Suite provides two main types of integrations:

Knowledge Bases: Retrieve data and knowledge from external applications for AI-powered search and analysis like Amazon Q Business , S3 , Microsoft Ondrive , Microsoft share point , etc.

Actions: Perform operations in other applications like MCPs, Asana , SAP , Salesforce , Microsoft 365 , Pagerduty , slack , smart sheet ,etc

Summary

Amazon QuickSuite is designed to cut through information overload and repetitive work, helping you rapidly build, deploy, and manage agentic AI workflows that deliver actionable insights and automation—all while ensuring security and governance. This marks a new frontier in how AI can work proactively as your teammate.

Learn more about Amazon QuickSuite

DEV Community: Venkata Pavan Vishnu Rachapudi

How Exporting AMIs to S3 Helped Me Recover Customer Resources During a DR Event

What is AMI Store and Restore?

Architecture Overview

Step 1 — Export the AMI to S3

Step 2 — Monitor the Export Task

Step 3 — Verify in S3

Step 4 — Replicate to Another Region

Step 5 — Restore the AMI

Step 6 — Launch the Instance

What Happens with KMS Encrypted AMIs?

When Should You Use This?

Disclaimer: Large AMI Exports May Take Time

Final Thoughts

Secure Remote Access to AWS Resources from On-Premises

Architecture Context

Flow 1: Application Access via AWS Verified Access

Use Case

Flow

Key Characteristics

Flow 2: Administrative EC2 Access via EC2 Instance Connect Endpoint

Use Case

Flow

Key Characteristics

Flow 3: Network-Level Access via AWS Client VPN

Use Case

Flow

Key Characteristics

VPN vs Verified Access (When to Use What)

Comparison Table

Simple Rule of Thumb

Why These Services Work Best Together

Security Best Practices Highlighted

Final Thoughts

CloudWatch Is No Longer Just for Logs: Inside AWS’s Unified Operational Data Store

The Big Idea: Logs Are Operational Data

Collect: Centralized, Org-Level Log Ingestion

What this enables

Why this matters

🔁 Collect Flow

Curate: Logs Engineering Becomes First-Class

What you can do

Why this is critical

🔁 Curate Flow

Analyze: CloudWatch + S3 Tables (The Game Changer)

Supported analysis tools

Why this is huge

🔁 Analyze Flow

Real CloudOps Use Cases

Example Flow: From VPC Flow Logs to Actionable Insights

End-to-End Flow (High Level)

Step-by-Step Breakdown

Collect – Org-Level Log Ingestion

Curate – Normalize & Enrich Logs

Persist – Materialize Logs into S3 Tables

Analyze – Ops & Security Use the Same Data

Incident + RCA Flow

Final Thoughts

Running AEM Without Losing Your Mind: A Practical Local & AWS EC2 Setup Guide

What CRX Actually Is (And Why You’ll Keep Hearing About It)

Running AEM Locally Using the Quickstart JAR

Before You Start

Folder Setup (Keep It Simple)

Starting the Author Instance

Starting the Publish Instance

Running AEM with Docker (When You Want Consistency)

Basic Folder Layout

A Simple Author Dockerfile

Build and Run

Run Modes: Small Flags, Big Impact

Common Ones You’ll Actually Use

How I Usually Set Them

Verify What’s Active

Debugging AEM (Yes, You Can Debug It Like Any Java App)

Enable Debug Port

Attach Your IDE

Logs You’ll Check Daily

The Three AEM Consoles You’ll Live In

CRXDE Lite (/crx/de)

Package Manager (/crx/packmgr)

CRXDE Lite (`/crx/de`)

Package Manager (`/crx/packmgr`)

OSGi System Console (`/system/console`)

2. Package Manager (`/crx/packmgr`)

3. OSGi System Console (`/system/console`)