🧠 Quick Story: What is happening here?

• You have Hadoop (big, distributed ETL workload).

• You have LOTS of EC2 instances (50 per AZ!).

• You want high availability (no single hardware failure should kill too many instances).

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🔥 Which Placement Group Should You Pick?

✅ Partition Placement Group

Why?

• It divides EC2 instances into partitions.

• Each partition uses different hardware.

• So, failures are isolated → If one partition fails, others are safe!

• Designed for distributed systems like Hadoop, Kafka, Cassandra.

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❌ Wrong Options (and why)

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🎯 Final Shortcut to Remember

Hadoop = Partition Placement Group.

Simple one-line memory rule:

"Distributed Big Data ➡️ Partition Placement Group"

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Would you also like a one-line trick to remember when to use Cluster vs Spread vs Partition?
I can give you a funny shortcut for that too! 🎯😄
Want it? 🚀

Awesome! 🚀 Here's a funny and super easy shortcut for remembering Cluster vs Spread vs Partition:

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🎯 1. Cluster Placement Group

"Cluster = Close"

• All instances packed closely together.

• For: Super fast, low-latency communication (like HPC apps, tightly-coupled systems).

🧠 Memory trick:

"Cluster means close together like a tight friend group."

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🎯 2. Spread Placement Group

"Spread = Separate"

• Instances are far apart — on different hardware.

• For: Protecting a small number of critical instances (max 7 per AZ).

🧠 Memory trick:

"Spread means spread out like introverts at a party."

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🎯 3. Partition Placement Group

"Partition = Pieces"

• Big groups divided into pieces (partitions).

• Each partition is isolated → good for Hadoop, Kafka, Big Data systems.

🧠 Memory trick:

"Partition means split into pieces for massive distributed workloads."

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🔥 Final One-Liner Summary:

Cluster = Close together 🔥
Spread = Separate far away 🏃‍♂️🏃‍♀️
Partition = Pieces for Big Data 🏗️

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🎯 What the company needs:

• Failover to AWS quickly if their on-premises data center fails.

• Minimal downtime (least waiting time).

• Same data on-prem and AWS (uniform data).

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❌ Why your selected option was wrong:

You selected a solution using AWS CloudFormation triggered by Lambda.
👉 Problem:
CloudFormation takes time to create EC2 instances, ALB, etc. (could be minutes).
Not instant failover ❌ — which increases downtime.

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✅ Correct (Best) Solution:

• Route 53 failover record
  (Automatically detects failure and switches traffic to AWS.)

• Already running EC2 servers behind an Application Load Balancer in an Auto Scaling group
  (Not creating them after failure — they are already ready!)

• AWS Storage Gateway
  (Keeps on-prem and AWS data in sync.)

👉 So no provisioning delay, failover is instant ✅.

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🧠 Easy memory trick:

Failover = Pre-Running + Load Balancer + Storage Sync 🚀
(Never launch servers during disaster. They must be already active.)

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📢 Simple Final Thought:

If the question asks for LEAST downtime, avoid any solution where new resources are created on-the-fly like CloudFormation or Lambda triggers.
Pre-built infrastructure wins every time! 🏆

🎯 What’s happening in the question:

• Videos are saved on local EBS volumes attached to each EC2 instance.

• When users log in, the Load Balancer sends them to different instances.

• Problem: Each instance has different videos on its own EBS.
  (That's why users see a random subset of their videos each time.)

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✅ Best solution:

You need shared storage that all EC2 instances can access together.
Two best options:

1. Amazon S3 (Best for Object Storage like videos)

• Upload all videos to S3.

• Modify app to read/write videos directly from S3.

• ✅ S3 is scalable, reliable, and all instances can access it anytime.

2. Amazon EFS (Shared File System)

• Mount EFS to all EC2 instances.

• Migrate existing videos from EBS to EFS.

• App can access videos like normal files but from shared storage.

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❌ Why other options are wrong:

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🧠 Easy memory trick:

For storing videos, images, files ➔ Use S3 or EFS, NOT databases.

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📢 Simple Final Thought:

Block storage (EBS) is tied to a single server.
Shared storage (S3 or EFS) is visible to all servers.

When users need to access the same content regardless of server, always think S3 or EFS first! 🚀

🏬 What’s happening in the question:

A retail company wants to test a blue-green deployment for its global app within 48 hours, just before a major sales event (Thanksgiving).

💡 Most users access the app via mobile devices, which often cache DNS records — making DNS-based changes slow to propagate.

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✅ Best answer:

Use AWS Global Accelerator to distribute a portion of traffic to a particular deployment

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✅ Why it's correct:

• AWS Global Accelerator lets you shift traffic instantly between deployments without waiting for DNS changes to propagate.

• Perfect for mobile clients, which cache DNS and may not update quickly.

• Provides global coverage and near-instant routing updates.

• Ideal for testing a new "green" version while keeping the "blue" live.

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❌ Why the other options are wrong:

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🔍 Blue-Green Deployment Options: Visual Comparison