Forem: Wakeup Flower

I'm back

Wakeup Flower — Wed, 11 Mar 2026 10:44:37 +0000

I got a big pause of some health issues, It feels great to be here, continue my learnning journey.

It's actually a big chance that my body works correctly so that I can continue piano, dancing and discovering the world and people in it.

I use Udemy for almost one year, I learned massive amount of things though being absent of everything for 3 months.

Please cherish your health too, it's everything for you !

Common image formats

Wakeup Flower — Tue, 21 Oct 2025 10:01:16 +0000

🧱 Raster Image Formats (Pixel-based)

These are the most common for photos, screenshots, and web images.

Format	Typical Extension	Best For	Key Traits
JPEG / JFIF	`.jpg`, `.jpeg`, `.jfif`	Photos, web images	Lossy compression → small size, slight quality loss
PNG	`.png`	Web graphics, transparent images	Lossless compression, supports transparency (alpha channel)
WEBP	`.webp`	Modern web use	Smaller than JPEG/PNG, supports transparency and animation; very common on websites today
HEIC / HEIF	`.heic`, `.heif`	iPhone photos	High efficiency (better quality at smaller size), supports depth and burst data
AVIF	`.avif`	Next-gen web & photography	Better compression and quality than WEBP; gaining adoption fast
GIF	`.gif`	Simple animations, memes	Limited color palette (256 colors), supports animation
BMP	`.bmp`	Windows bitmaps (legacy)	Uncompressed, very large file size
TIFF / TIF	`.tiff`, `.tif`	Printing, scanning, photography	High-quality, supports layers, lossless, large files
RAW formats	`.cr2`, `.nef`, `.arw`, etc.	DSLR / Mirrorless cameras	Unprocessed image data; huge files, best for editing

🧩 Vector Image Formats (Shape-based)

Used for logos, icons, and scalable designs.

Format	Extension	Best For	Key Traits
SVG	`.svg`	Web icons, graphics, illustrations	Scalable (no quality loss), editable with code or design tools
EPS	`.eps`	Printing, professional graphics	Legacy vector format for Adobe software
PDF (vector images)	`.pdf`	Documents and printable graphics	Can embed text, vector art, and images

🕹️ Special / Modern Use

Format	Extension	Use
ICO	`.ico`	Windows icons
WEBM	`.webm`	Animated or video-like web content (also used in short animations)
PSD	`.psd`	Adobe Photoshop layered images
EXR	`.exr`	Film / VFX industry (high dynamic range)

🚀 Most Common on the Web (2025)

WEBP → dominant (Google Chrome, Edge, Firefox, Safari support it)
JPEG → still very widespread
PNG → for transparency and UI icons
AVIF → fast-growing, especially on modern browsers and phones

From Doubt to Triumph: AWS Certified Solutions Architect Exam

Wakeup Flower — Fri, 17 Oct 2025 10:47:13 +0000

Yesterday, I officially passed the AWS Solutions Architect Associate (SAA-C03) exam !

It definitely wasn’t easy — at times, it felt like climbing a mountain blindfolded. Since English isn’t my first language, understanding all the small details in those long, tricky questions was tough. And to make things worse, I completely forgot to add the extra 30 minutes for non-native speakers. You can’t imagine how much I blamed myself for that mistake.

But in the end… I did it.

It was such an emotional roller coaster — nerves, frustration, hope, and finally, relief.

Now that this chapter is done, I’m going to take a short break before starting my next goal: the AWS Certified DevOps Engineer – Professional (DOP-C02) in 2026.

Lake Formation workflow of RDS & S3

Wakeup Flower — Tue, 14 Oct 2025 08:39:48 +0000

🌊 Goal:

Create a data lake that combines data from:

Amazon RDS (relational database)
Amazon S3 (object storage for large-scale analytics)

You’ll then use AWS Lake Formation to centrally manage data access and security — without manually writing IAM policies or complex permissions.

🔧 Step-by-Step Breakdown

1️⃣ Create a Data Lake with Lake Formation

Lake Formation is built on top of AWS Glue and Amazon S3 — it helps you:

Catalog all your data sources (S3, RDS, Redshift, etc.)
Define fine-grained access controls (at the table, column, or row level)
Use a single, centralized permission model for analytics services (like Athena, Redshift Spectrum, EMR, Glue)

How:

In the Lake Formation console, register your data lake location (usually an S3 bucket or a prefix, e.g. s3://company-data-lake/).
Lake Formation creates a data catalog (via AWS Glue Data Catalog) that tracks where data lives and its schema.

2️⃣ Create an AWS Glue JDBC Connection to Amazon RDS

JDBC ( Java Database Connectivity )

It’s a standard protocol that lets software applications connect to databases — regardless of the database vendor (MySQL, PostgreSQL, Oracle, SQL Server, etc.).

This allows AWS Glue (and by extension, Lake Formation) to connect directly to your Amazon RDS database and crawl or extract metadata.

How:

In AWS Glue, go to Connections → Add connection
Choose JDBC
Enter your RDS endpoint, database name, username, and password (stored securely in AWS Secrets Manager)
Ensure your Glue job or crawler runs in the same VPC as your RDS instance, or that it has network access.

Result: Glue can now read schema and data from RDS tables.

3️⃣ Register the S3 Bucket in Lake Formation

This step “tells” Lake Formation that this S3 bucket is part of your managed data lake.

Why it matters:

Lake Formation then enforces fine-grained access controls on that bucket.
Any other AWS analytics service (Athena, Redshift Spectrum, etc.) must check Lake Formation permissions before reading that S3 data.

How:

In Lake Formation → Data lake locations → Register location
Choose your S3 bucket (e.g., s3://company-data-lake/)
Assign a “data lake administrator” who can manage permissions.

4️⃣ Use Lake Formation Access Controls

Now you can define who can see or query what data.

Examples:

Allow the data science team to read only the sales table.
Allow auditors to read only the transactions table, but hide the customer_email column.
Allow marketing to query summary tables but not raw data.

Lake Formation integrates with:

Amazon Athena
Amazon Redshift Spectrum
Amazon EMR
AWS Glue

Each of these services checks Lake Formation permissions before accessing data.

🔒 Access Control Example

User	Resource	Permission
Data Scientist	`s3://data-lake/sales/`	SELECT
Analyst	`transactions` table	SELECT (no access to PII columns)
Admin	All databases/tables	FULL_ACCESS

💡 Why this Architecture?

✅ Centralized data governance — fine-grained permissions, all permissions managed in Lake Formation.
✅ Unified analytics — combine RDS (structured data) and S3 (semi/unstructured data).
✅ Scalable & cost-effective — query with Athena or Redshift Spectrum directly on S3.
✅ Minimal coding — Glue handles schema discovery; Lake Formation handles permissions.

          ┌───────────────────────┐
          │ Amazon RDS (MySQL)    │
          │ Customer Data          │
          └──────────┬────────────┘
                     │ JDBC Connection (AWS Glue)
                     ▼
             ┌───────────────────────────┐
             │ AWS Glue Data Catalog      │
             │ Metadata for RDS + S3      │
             └──────────┬────────────────┘
                        │
                        ▼
             ┌───────────────────────────┐
             │ AWS Lake Formation         │
             │ - Unified Data Lake        │
             │ - Fine-grained access ctrl │
             └──────────┬────────────────┘
                        │
          ┌─────────────┴────────────────────┐
          │       Amazon Athena / BI Tools   │
          │     Query Across RDS + S3 Data   │
          └─────────────────────────────────┘

Step	Component	Role
1️⃣	S3 + RDS	Raw data sources
2️⃣	AWS Glue JDBC	Connects RDS to data catalog
3️⃣	Lake Formation	Creates unified data lake with security control
4️⃣	Athena / QuickSight	Analyze both sources together
5️⃣	Result	Secure, centralized, scalable data access

Compare Glue, Data Pipeline & Step functions

Wakeup Flower — Mon, 13 Oct 2025 16:05:43 +0000

1. AWS Data Pipeline

Purpose: Orchestrates data movement and batch ETL workflows between AWS services or on-premises.
Focus: Scheduling and automating data flows, not performing transformations itself (though it can trigger EMR jobs, SQL scripts, or Lambda).
Best for: Multi-step data pipelines across services on a schedule.

Example: Move data from on-prem Oracle → S3 → Redshift every night.

2. AWS Glue

Purpose: Fully-managed ETL service for data cataloging, transformation, and loading.
Focus: Data processing and transformation. It can also orchestrate ETL jobs, but it’s more about preparing data for analytics than just moving it.
Best for: Automated, serverless ETL, especially when using Spark to process large datasets.
Components:
- Glue Data Catalog – keeps metadata of datasets.
- Glue ETL Jobs – process data using Spark/PySpark.
- Glue Crawlers – automatically detect schema and update the catalog.

Example: Transform raw S3 logs → normalize fields → write to Redshift for analytics.

3. AWS Step Functions

Purpose: Orchestrates application workflows and serverless services.
Focus: Task coordination, conditional logic, branching, retries, parallel execution, human approval.
Best for: Business/application workflows, not specifically data ETL (though it can orchestrate ETL jobs).

Example: Order workflow: receive order → charge payment → update inventory → notify user.

Comparison Table

Feature	AWS Data Pipeline	AWS Glue	AWS Step Functions
Primary Use	Orchestrate batch data movement	ETL: catalog, transform, load	Orchestrate application workflows
Trigger	Scheduled or on-demand	Scheduled, on-demand, or event-based	Event-driven, API, or scheduled
Logic	Sequential, basic retry	ETL transformations, partitioning	Sequential, parallel, branching, error handling
Services	S3, EMR, RDS, DynamoDB	S3, Redshift, RDS, JDBC sources	Lambda, ECS, Batch, Glue, DynamoDB, SNS, SQS
Transformations	Limited (via jobs)	Rich transformations with Spark	Optional, via Lambda or services
Metadata	No	Glue Data Catalog	No
Best For	Moving/transforming data pipelines	Preparing analytics-ready data	Coordinating services / microservices

Analogy

Data Pipeline → Conveyor belt moving data between factories.
Glue → The machine on the belt that cleans, transforms, and prepares the data.
Step Functions → Project manager coordinating tasks, teams, and decisions across the company.

✅ Exam Tip:

If the question is about orchestrating ETL for analytics, Glue is usually the answer.
If it’s about scheduled data movement (no heavy transformations), think Data Pipeline.
If it’s about application workflow orchestration, branching, retries, human approvals → Step Functions.

When you can't use serverless DMS

Wakeup Flower — Mon, 13 Oct 2025 15:04:29 +0000

Serverless DMS tasks remove the need to manage replication instances while still supporting full-load + CDC migrations.

** CDC stands for Change Data Capture **
It’s a mechanism to track and capture changes (inserts, updates, deletes) in a database so that they can be replicated or processed elsewhere.

Think of CDC like a live news feed of database changes. Once you have the full story (full load) + CDC keeps sending you all the updates as they happen.

Feature	Serverless DMS	Traditional DMS
Replication instance	❌ managed by AWS	✅ provisioned by user
Scalability	Auto-scaled	Manual scaling required
Cost	Pay per use	Pay hourly for instance
Maintenance	Minimal	User responsible for patching, sizing
Use case	Sporadic migration, cloud-native, low ops	Continuous heavy workloads, fine-tuned control

You cannot use serverless DMS in certain situations where the limitations of the service prevent it from working. Here’s a detailed breakdown:

1. Unsupported Database Engines

Serverless DMS does not support all source and target engines.

Category	Supported?
Aurora MySQL	✅
RDS MySQL	✅
Aurora PostgreSQL	✅
RDS PostgreSQL	✅
Oracle	❌ (serverless not available)
SQL Server	❌
SAP, Db2, MariaDB	❌

If your migration uses unsupported engines, you must use a provisioned DMS replication instance.

2. Very Large Workloads

Serverless DMS has limits on database size and throughput.
For very large databases (e.g., hundreds of TBs) or high CDC volume, a provisioned replication instance may be needed for predictable performance.

3. Advanced Configurations

Serverless DMS cannot handle some advanced replication features, such as:

Multi-AZ replication configuration (for high availability).
Fine-grained control over replication instance type, storage, and scaling.
Large-scale transformations that require custom pre/post-processing tasks.

4. On-Premises Databases Without AWS Direct Connectivity

If the source database cannot connect reliably to AWS over the network (VPN, Direct Connect, or public endpoints), serverless DMS may fail.
Provisioned DMS allows more tuning options for network throughput and retry strategies.

Summary Table

Limitation	When Serverless DMS Won’t Work	Alternative
Database engine unsupported	Oracle, SQL Server, MariaDB	Use provisioned DMS instance
Large-scale workload	Very large databases, high CDC	Provisioned instance
Advanced replication features	Multi-AZ, fine-grained control	Provisioned instance
Connectivity limitations	On-prem without stable network	Provisioned instance with VPN/Direct Connect

✅ Key Takeaway:
Serverless DMS is best for small-to-medium workloads, cloud-native databases, and low operational overhead, but for unsupported engines, very large databases, or advanced configurations, you must use a provisioned replication instance.

If you want, I can make an easy-to-remember diagram comparing Serverless DMS vs Provisioned DMS and when to use each, which is very exam-friendly.

Do you want me to do that?

Connect to EC2 without SSH

Wakeup Flower — Mon, 13 Oct 2025 14:35:19 +0000

Attach the AmazonSSMManagedInstanceCore IAM policy to an IAM role that is associated with the EC2 instances. Instruct the developers to use AWS Systems Manager Session Manager to access the EC2 instances.

Yes! You can use AWS Systems Manager Session Manager from your PC without SSH, and without going through the AWS console. Here’s how:

1. Install AWS CLI

Make sure the AWS CLI v2 is installed on your PC.
Configure it with your AWS credentials that have permission to use Session Manager (ssm:StartSession).

aws configure

2. Start a Session

Run the following command to connect to your EC2 instance:

aws ssm start-session --target <instance-id>

This opens an interactive shell on the EC2 instance, just like SSH.

3. Optional: Use Session Manager Plugin

Some systems may require the Session Manager plugin for CLI to open sessions.
Install it from AWS: https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.html

Advantages over SSH

Feature	SSH	Session Manager
No Internet Needed	❌ (unless bastion host)	✅ (works in private subnet)
No Key Management	❌	✅ (IAM-based access)
Audit Logging	❌ (manual)	✅ (CloudTrail logs)
Remote Access	❌	✅ (CLI or console)

Summary

From your PC, Session Manager works like SSH, but uses IAM credentials instead of keys.
No need for bastion hosts or VPNs if the instance is in a private subnet.
Fully secure and auditable.

2 LB algorithms Round Robin & Least Outstanding Requests

Wakeup Flower — Mon, 13 Oct 2025 12:37:14 +0000

Scenario

Web app behind an Application Load Balancer (ALB).
Some EC2 instances are overloaded (too many outstanding requests).
CloudWatch shows higher:
- Request count
- Response time
Requirement: Do not forward new requests to overloaded instances.

Key background:

ALBs support two main load-balancing algorithms:

Round Robin (default) – evenly distributes requests, without considering instance load.
Least Outstanding Requests (LOR) – sends new requests to the target with the fewest active (in-flight) requests, providing adaptive load distribution.

When some instances get slower (more outstanding requests), LOR automatically directs new traffic to the less busy instances.

Metric relevance

RequestCountPerTarget → shows how many requests each target handled.
ActiveConnectionCount → number of open connections (relevant for HTTP keep-alives or WebSockets).
TargetResponseTime → average response time, good for observation but not for routing logic directly.

The ALB’s Least Outstanding Requests algorithm inherently considers the number of active requests per target—it doesn’t need CloudWatch metrics directly for routing. But among the given choices, the ones referencing ActiveConnectionCount and RequestCountPerTarget correctly describe load indicators.

Option analysis

Option	Algorithm	Metrics	Notes
A	Round robin	RequestCountPerTarget + ActiveConnectionCount	❌ Round robin ignores load; not suitable.
B	Least outstanding requests	RequestCountPerTarget + ActiveConnectionCount	✅ Correct — uses LOR algorithm, which addresses overloaded instances.
C	Round robin	RequestCount + TargetResponseTime	❌ Round robin still ignores load.
D	Least outstanding requests	RequestCount + TargetResponseTime	❌ “TargetResponseTime” not used by the ALB algorithm; LOR uses outstanding request count.

Why:

The Least Outstanding Requests algorithm automatically avoids sending new requests to busy instances.
Metrics like RequestCountPerTarget and ActiveConnectionCount confirm the balancing effectiveness, but the ALB handles this logic internally.

Route 53 Resolver outbound endpoint

Wakeup Flower — Mon, 13 Oct 2025 12:30:24 +0000

🔹 What an outbound endpoint does

A Route 53 Resolver outbound endpoint allows DNS queries that originate inside your VPC (for example, from EC2 instances or AWS services) to be forwarded to external DNS servers — typically, on-premises DNS resolvers — through a VPN or AWS Direct Connect.

🔹 When to use an outbound endpoint

You use it whenever your workloads in AWS need to resolve private DNS names hosted outside AWS, such as:

On-premises Active Directory domains
- e.g., AWS EC2 instances need to resolve server1.corp.local.
Hybrid environments where part of your application runs in AWS and part on-prem, and they share internal DNS zones.
Multi-VPC or multi-region setups where one environment needs to query DNS zones owned by another that isn’t accessible directly via Route 53 Private Hosted Zones.
Custom DNS servers running in another network (for example, a security appliance or custom name-resolution logic).

🔹 Typical architecture

Your AWS VPC has resources (like EC2 instances or ECS tasks).
Those resources need to resolve names that live on-premises.
You create a Route 53 Resolver outbound endpoint in the VPC (requires two or more ENIs in different subnets for high availability).
You create a resolver rule — e.g.,

Domain: corp.local
Forwarding target: 10.0.1.10 (on-prem DNS server)
1. Associate that rule with the VPC.
2. DNS queries for corp.local are automatically forwarded securely over the VPN/Direct Connect link to the on-prem resolver.

🔹 **When not to use it**

You don’t need an outbound endpoint if:

All your private zones are hosted in Route 53 Private Hosted Zones and don’t depend on on-prem DNS.
You only need on-prem → AWS resolution (then you’d use an inbound endpoint instead).
You’re using a centralized DNS design where one VPC acts as the shared resolver for others (those VPCs can use Resolver rules and peering instead).

🔹 Summary Table

Use case	Resolver type	Direction	Example
AWS needs to resolve on-prem/private DNS	Outbound Endpoint	AWS → On-Prem	`ec2 → server.corp.local`
On-prem needs to resolve AWS private DNS	Inbound Endpoint	On-Prem → AWS	`onprem → app.internal.aws`

✅ In short:
Use a Route 53 Resolver outbound endpoint whenever AWS resources need to resolve DNS names that live outside of AWS (e.g., on-prem or external private zones) securely and automatically.

What's "AWS Local Zones"

Wakeup Flower — Sun, 12 Oct 2025 20:09:51 +0000

AWS Local Zones

Definition:
AWS Local Zones are extensions of an AWS Region that place compute, storage, database, and other services physically closer to end users in specific metropolitan areas.

Key Characteristics

Low-latency access

Designed for applications that need single-digit millisecond latency to end users in a specific city or metro area.
Example: Real-time gaming, video streaming, financial trading apps.

Extension of a parent region

Each Local Zone is connected to its parent AWS Region (e.g., eu-central-1).
You can extend your VPC from the parent region into the Local Zone.

Supports dynamic compute

You can run EC2 instances, EKS nodes, RDS, and other services in a Local Zone.
Unlike CloudFront, Local Zones run actual application compute, not just caching.

Services available

EC2, EBS, VPC, RDS, ECS, EKS, and some other services.
Not every AWS service is available in a Local Zone — it’s focused on low-latency workloads.

Use Cases

Use Case	Why Local Zones help
Real-time gaming	Low latency to players in a city
Live video streaming	Encode and process video near users
Financial trading apps	Millisecond-level market data processing
AR/VR applications	Low latency is critical for immersive experiences

Analogy

Think of Local Zones like a mini AWS data center in your city:

Parent region (e.g., Frankfurt eu-central-1) → main data center
Local Zone (e.g., Berlin) → a “branch office” data center closer to users
Your VPC spans both → compute runs in Berlin, data stays in Frankfurt

Key Exam Tip

Dynamic, latency-sensitive apps → AWS Local Zones
Static content or caching → CloudFront edge locations

AWS Local Zones are used when your application requires extremely low latency for end users in a specific city or metro area. Think of workloads that can’t tolerate the network round-trip time to a full AWS Region.

1. Real-time applications

Gaming: Multiplayer games where milliseconds matter for player actions.
Live streaming / broadcasting: Video encoding and processing near viewers.
AR/VR applications: High interactivity demands single-digit millisecond latency.

2. Financial and trading applications

Stock trading, high-frequency trading: Local Zones can host compute near stock exchanges to reduce latency.
Market data processing: Fast ingestion and response times are critical.

3. Hybrid or edge workloads

On-premises integration: Users in a city can access resources in a Local Zone instead of traveling all the way to the parent region.
Latency-sensitive data processing: Local preprocessing before sending data to the main region.

4. Software development / testing

Low-latency testing environments: Developers need to test apps under conditions close to the end user.
Can be used to mimic real-world latency scenarios for metro-specific deployment.

5. High-performance computing (HPC) near users

Simulation or rendering workloads: Compute-intensive tasks that need fast interaction with local data sources.

Key Takeaways

Not for general workloads: If latency is not critical, use the main region.
Dynamic compute only: CloudFront is for static caching; Local Zones can run EC2, EKS, RDS, etc.
Best for cities far from your parent region or with strict latency SLAs.

AWS Instance Scheduler

Wakeup Flower — Sat, 11 Oct 2025 10:42:28 +0000

1. Services Supported by Instance Scheduler

Service	Resource Type	Notes
Amazon EC2	Instances	Start/stop instances in a VPC or public subnet based on schedule
Amazon RDS	DB instances	Start/stop RDS database instances (Aurora, PostgreSQL, MySQL, SQL Server, etc.)
Amazon Redshift	Clusters	Start/stop Redshift clusters
Amazon Aurora	DB clusters	Aurora DB clusters can also be scheduled (stopping/starting in supported regions)

Basically, Instance Scheduler handles resources that support start and stop operations.

2. How Instance Scheduler Accesses Services

Deployed via AWS CloudFormation template.
Creates an IAM role with permissions to:
- Start/stop resources (ec2:StartInstances, rds:StartDBInstance, etc.)
- Read tags and resource metadata
- Write logs to CloudWatch Logs
Scheduler uses tags on resources to decide which resources to manage.
- Example: Schedule=BusinessHours

3. Key Features

Feature	Description
Tag-based targeting	Only resources with a specific tag are scheduled
Multiple schedules	Can define different schedules (business hours, weekends, holidays)
Monitoring & logs	Uses CloudWatch logs to track start/stop operations
Flexible timing	Supports cron expressions, UTC offsets, and time zones

4. Important Notes

Works only with services that support start/stop; cannot schedule services that are always running (like DynamoDB, Lambda, S3).
Aurora Serverless v2 doesn’t need Instance Scheduler — it auto-scales.

✅ Summary Table

Question	Answer
Supported resources	EC2, RDS (instances & Aurora clusters), Redshift
Access method	IAM role created by CloudFormation
Selection method	Tag-based (`Schedule` tag by default)
Scheduling flexibility	Cron expressions, time zones, multiple schedules

Lambda SnapStart reduces cold start latency (java)

Wakeup Flower — Sat, 11 Oct 2025 10:24:32 +0000

1. What Lambda SnapStart Is

AWS Lambda functions, especially Java, can have long cold start times because the runtime has to initialize the code and dependencies.
SnapStart solves this by initializing the function ahead of time and taking a snapshot of the initialized execution environment.
When a new invocation occurs, Lambda reuses the snapshot instead of starting from scratch.

In short: cold starts become much faster.

2. How It Works

Deploy your Lambda function with SnapStart enabled.
When you publish a version:

Lambda runs the function once.
Lambda takes a snapshot of the memory, execution environment, and initialized state.
1. On subsequent invocations:
Lambda restores the snapshot.
Skips the normal initialization phase (runtime boot + static initializations).

Without SnapStart:   Cold Start → Runtime Init → Code Init → Handler Execute
With SnapStart:      Cold Start → Snapshot Restore → Handler Execute

Result: cold start latency can drop from seconds to milliseconds, especially for Java and other slow-start runtimes.

3. Key Points

Feature	SnapStart
Supported Runtimes	Java (as of now), others may be added
Applies To	Lambda function versions (not aliases directly)
Use Case	Functions with heavy initialization (DB connections, libraries, frameworks)
Cold Start Improvement	70–95% faster in typical cases
Cost Impact	Minimal — you still pay normal Lambda invocation charges

4. Comparison With Other Optimizations

Optimization	How It Helps	Notes
Provisioned Concurrency	Keeps pre-initialized instances warm	Fixed cost even if unused
Lambda SnapStart	Creates snapshots for new invocations	Only triggers on version publish, cheaper than provisioned concurrency
Both	Can be combined	Maximize performance and minimize cold starts

5. TL;DR

Lambda SnapStart = pre-initialized snapshot of your Lambda function,
which dramatically reduces cold start time, especially for Java functions.

Forem: Wakeup Flower

I'm back

Common image formats

🧱 Raster Image Formats (Pixel-based)

🧩 Vector Image Formats (Shape-based)

🕹️ Special / Modern Use

🚀 Most Common on the Web (2025)

From Doubt to Triumph: AWS Certified Solutions Architect Exam

Lake Formation workflow of RDS & S3

🌊 Goal:

🔧 Step-by-Step Breakdown

1️⃣ Create a Data Lake with Lake Formation

2️⃣ Create an AWS Glue JDBC Connection to Amazon RDS

3️⃣ Register the S3 Bucket in Lake Formation

4️⃣ Use Lake Formation Access Controls

🔒 Access Control Example

💡 Why this Architecture?

Compare Glue, Data Pipeline & Step functions

1. AWS Data Pipeline

2. AWS Glue

3. AWS Step Functions

Comparison Table

Analogy

When you can't use serverless DMS

1. Unsupported Database Engines

2. Very Large Workloads

3. Advanced Configurations

4. On-Premises Databases Without AWS Direct Connectivity

Summary Table

Connect to EC2 without SSH

1. Install AWS CLI

2. Start a Session

3. Optional: Use Session Manager Plugin

Advantages over SSH

Summary

2 LB algorithms Round Robin & Least Outstanding Requests

Scenario

Key background:

Metric relevance

Option analysis

Route 53 Resolver outbound endpoint

🔹 What an outbound endpoint does

🔹 When to use an outbound endpoint

🔹 Typical architecture

🔹 When not to use it

🔹 Summary Table

What's "AWS Local Zones"

AWS Local Zones

Key Characteristics

Use Cases

Analogy

Key Exam Tip

1. Real-time applications

2. Financial and trading applications

3. Hybrid or edge workloads

4. Software development / testing

5. High-performance computing (HPC) near users

Key Takeaways

AWS Instance Scheduler

1. Services Supported by Instance Scheduler

2. How Instance Scheduler Accesses Services

3. Key Features

4. Important Notes

✅ Summary Table

Lambda SnapStart reduces cold start latency (java)

1. What Lambda SnapStart Is

2. How It Works

3. Key Points

4. Comparison With Other Optimizations

5. TL;DR

🔹 **When not to use it**