Amazon Web Services (AWS) is the world’s leading cloud computing platform that offers a wide range of cloud-based services, including computing power, storage, networking, databases, machine learning, and security. AWS enables businesses, startups, and enterprises to build scalable, cost-effective, and secure applications without having to invest in on-premises infrastructure. With over 200 fully featured services across data centers globally, AWS is used by millions of organizations to enhance operational efficiency and drive innovation.

What is AWS?

AWS is a comprehensive cloud computing platform developed by Amazon that provides Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) solutions. AWS offers a pay-as-you-go pricing model, allowing organizations to only pay for the resources they use. It supports businesses across various industries, including healthcare, finance, education, gaming, and artificial intelligence.

Key Characteristics of AWS:

• Highly Scalable: Offers automatic scaling for workloads and applications.
• Secure & Compliant: Provides enterprise-level security with compliance certifications.
• Cost-Effective: Reduces IT costs by offering flexible pricing options.
• Global Infrastructure: Spans multiple availability zones and regions worldwide.
• Innovative Technologies: Supports AI, IoT, blockchain, and analytics.

Top 10 Use Cases of AWS

1. Website Hosting & Content Delivery
   • AWS enables businesses to host static and dynamic websites with services like Amazon S3, Amazon EC2, and AWS CloudFront.
2. Big Data Analytics
   • AWS services such as Amazon Redshift, AWS Glue, and AWS Athena help businesses process and analyze large datasets efficiently.
3. Machine Learning & AI
   • AWS provides pre-trained AI models and machine learning frameworks through services like Amazon SageMaker, AWS DeepLens, and AWS Lex.
4. Internet of Things (IoT)
   • AWS IoT Core and AWS Greengrass allow organizations to securely connect and manage IoT devices at scale.
5. Cloud Storage & Backup Solutions
   • Amazon S3, AWS Glacier, and AWS Backup provide reliable storage and backup solutions with high availability.
6. DevOps & Continuous Integration/Continuous Deployment (CI/CD)
   • AWS CodePipeline, AWS CodeBuild, and AWS Lambda facilitate CI/CD pipelines for faster application development and deployment.
7. Enterprise Applications & ERP Solutions
   • Businesses use AWS to host ERP software like SAP and Oracle, reducing costs and increasing efficiency.
8. Gaming & Media Streaming
   • AWS services like Amazon GameLift and AWS Elemental enable seamless online gaming and video streaming experiences.
9. Disaster Recovery & Business Continuity
   • AWS ensures data redundancy and business continuity through multi-region backup and recovery solutions.
10. Blockchain & Cryptocurrency

• AWS supports blockchain solutions for secure transactions using Amazon Managed Blockchain and AWS Quantum Ledger Database.

Features of AWS

1. Elastic Compute Cloud (EC2) – Scalable virtual servers for hosting applications and workloads.
2. Simple Storage Service (S3) – Secure and scalable object storage for backup, archive, and data sharing.
3. AWS Lambda – Serverless computing for running applications without managing infrastructure.
4. AWS CloudFormation – Automates infrastructure provisioning using templates.
5. Amazon RDS (Relational Database Service) – Fully managed databases like MySQL, PostgreSQL, and Oracle.
6. AWS Identity and Access Management (IAM) – Controls access permissions for AWS services and resources.
7. AWS Auto Scaling – Automatically scales applications to handle varying traffic loads.
8. Amazon DynamoDB – NoSQL database for high-performance applications.
9. AWS Virtual Private Cloud (VPC) – Secure cloud networking and private IP address management.
10. Amazon CloudWatch – Monitoring and logging service for AWS applications and infrastructure.

How AWS Works and Architecture

1. AWS Global Infrastructure

• AWS operates in multiple regions, availability zones (AZs), and edge locations worldwide.
• Each region consists of multiple AZs to ensure fault tolerance and disaster recovery.

2. Compute Services

• AWS EC2 instances provide virtual machines for running applications.
• AWS Lambda offers serverless computing to run code without managing servers.

3. Storage Services

• AWS S3 provides scalable object storage.
• Amazon EBS (Elastic Block Store) is used for persistent storage attached to EC2 instances.

4. Networking & Content Delivery

• AWS VPC allows users to create private cloud networks.
• AWS CloudFront delivers content with low latency using a global CDN.

5. Security & Compliance

• AWS IAM ensures secure access control.
• AWS Shield provides protection against DDoS attacks.

How to Install AWS

It seems like you’re asking how to install AWS CLI (Amazon Web Services Command Line Interface) or use AWS resources programmatically via code, but the phrase “AWS in coe” isn’t entirely clear. I’ll assume you’re referring to installing and configuring the AWS CLI or interacting with AWS services using programming code (such as Python, Terraform, etc.).

1. Installing AWS CLI

The AWS CLI (Command Line Interface) is a tool that allows you to interact with AWS services from your terminal. Here’s how to install AWS CLI:

Step 1: Install AWS CLI (Version 2)

For Windows:

1. Download the AWS CLI installer for Windows from AWS CLI download page.
2. Run the installer and follow the prompts.

For macOS:

You can install AWS CLI using Homebrew:

brew install awscli

Alternatively, use the official installer:

curl "https://awscli.amazonaws.com/awscli-exe-macos-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

For Linux (Ubuntu/Debian-based):

To install AWS CLI on Linux, run:

# Download and install AWS CLI v2
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

Verify Installation:

After installation, verify that AWS CLI is installed properly by running:

aws --version

You should see an output similar to:

aws-cli/2.x.x Python/3.x.x Linux/4.x.x

2. Configure AWS CLI

Once installed, you need to configure the AWS CLI with your AWS credentials (Access Key and Secret Key).

aws configure

You’ll be prompted to enter the following:

• AWS Access Key ID: You can find this in your AWS Console under IAM (Identity and Access Management).
• AWS Secret Access Key: This will also be available in the IAM section.
• Default Region Name: This is the region you typically use, e.g., us-west-2.
• Default Output Format: Usually set to json, but you can choose text or table.

3. Install AWS SDK (For Programming Code)

If you’re interacting with AWS services programmatically, you can use AWS SDKs. Here’s how to use Python (boto3) as an example.

Step 1: Install boto3 (AWS SDK for Python)

You can install boto3, the AWS SDK for Python, using pip:

pip install boto3

Step 2: Example Python Code to Interact with AWS

Once boto3 is installed, you can write Python code to interact with AWS services.

Here’s an example Python script that lists all EC2 instances in your AWS account:

import boto3

# Create a session using your AWS credentials
ec2 = boto3.client('ec2')

# Describe EC2 instances
response = ec2.describe_instances()

# Print instance details
for reservation in response['Reservations']:
    for instance in reservation['Instances']:
        print(f"ID: {instance['InstanceId']}, Type: {instance['InstanceType']}, State: {instance['State']['Name']}")

Step 3: Verify Authentication

Before using the SDK, ensure you’re authenticated using AWS CLI with the aws configure command or by setting up your credentials file.

Alternatively, you can provide your AWS Access Key ID and Secret Access Key programmatically using:

import boto3

# Use AWS access keys directly (if not using configured profile)
ec2 = boto3.client('ec2', aws_access_key_id='your-access-key',
                  aws_secret_access_key='your-secret-key', region_name='us-west-2')

However, using IAM roles and AWS CLI configuration is the recommended and safer approach.

4. Automate AWS Infrastructure with Terraform

You can use Terraform to provision and manage AWS resources. Here’s an example of provisioning an EC2 instance with Terraform:

Step 1: Install Terraform

Download and install Terraform from the official site.

For Linux (Ubuntu):

sudo apt-get update
sudo apt-get install terraform

For macOS:

brew install terraform

Step 2: Configure Terraform to Use AWS

Create a main.tf file to configure an AWS provider and resource.

# Configure AWS provider
provider "aws" {
  region = "us-west-2"
}

# Provision an EC2 instance
resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"  # Use your preferred AMI ID
  instance_type = "t2.micro"

  tags = {
    Name = "MyInstance"
  }
}

Step 3: Apply Terraform Configuration

Initialize and apply the Terraform configuration:

terraform init
terraform apply

This will provision the EC2 instance on AWS based on the configuration.

5. Monitor and Manage AWS with CloudWatch and CloudTrail

You can use CloudWatch to monitor AWS services and CloudTrail to log API activity.

For example, using AWS CLI to create a CloudWatch alarm:

aws cloudwatch put-metric-alarm --alarm-name "HighCPUAlarm" \
  --metric-name "CPUUtilization" --namespace "AWS/EC2" \
  --statistic "Average" --period 300 --threshold 80 \
  --comparison-operator "GreaterThanThreshold" \
  --dimensions "Name=InstanceId,Value=i-12345678" \
  --evaluation-periods 2 --alarm-actions arn:aws:sns:us-west-2:123456789012:MyTopic

This creates an alarm that triggers an SNS notification if CPU utilization exceeds 80%.

Basic Tutorials of AWS: Getting Started

Step 1: Create an EC2 Instance

1. Log in to the AWS Management Console.
2. Navigate to EC2 > Launch Instance.
3. Select an Amazon Machine Image (AMI) (e.g., Ubuntu, Windows Server).
4. Choose an Instance Type (e.g., t2.micro for free tier).
5. Configure security groups and launch the instance.

Step 2: Create an S3 Bucket

1. Go to S3 Service in AWS.
2. Click Create Bucket, set a unique bucket name, and choose a region.
3. Configure permissions and upload files.

Step 3: Deploy a Serverless Function with AWS Lambda

1. Open AWS Lambda from the AWS Console.
2. Click Create Function and select Author from Scratch.
3. Choose a runtime (e.g., Python, Node.js).
4. Upload your function code and deploy.

Step 4: Set Up a CloudWatch Monitoring Dashboard

1. Go to Amazon CloudWatch.
2. Click Create Dashboard.
3. Add widgets for CPU Usage, Memory Utilization, and Network Metrics.

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Oracle launched an observability and management platform that can conduct those deeds across virtually any infrastructure construct and tackle what has been an ongoing challenge for organizations in attempting to see what is exactly happening within their dispersed workloads.

The new Cloud Observability and Management Platform is available through Oracle Cloud Infrastructure (OCI). It allows users to monitor cloud-native and traditional software deployments across their multi-cloud infrastructure down to their on-premises locations.

Clay Magouyrk, EVP for cloud infrastructure engineering at Oracle, explained in a blog post that the platform begins by using a logging tool to scrape all of the necessary information into a single repository. That logging tool uses the open source Fluentd collector to pool the data in a Cloud Native Computing Foundation (CNCF) CloudEvents-compatible format for easier parsing and view of that data.

Users can also set their own rules for acting on that data, including using Oracle’s Streaming service to send logs to any destination. The Oracle logging tool also uses machine learning to detect any issues and provides possible fixes to those problems in real time.

Magouyrk said that the platform’s ability to log data from across different environments is what sets it apart from competing offers like Amazon Web Services (AWS) CloudWatch and Microsoft Azure Monitor.

The platform’s monitoring component provides what Magouyrk described as “real and synthetic end-user monitoring, server monitoring, and distributed tracing, compatible with the open source OpenTracing and OpenMetrics frameworks.” This allows a user to monitor down to the end-user experience for each interaction, which is common in a microservices-based environment.

Workload Spread

This ability to observe workloads across different environments is becoming more important for organizations that want to operate in a multi-cloud environment. Those moves are becoming even more dispersed as those distributed workloads also begin operating in smaller subsets of those clouds like virtual machines (VMs), containers, and serverless.

“The problem with all this is that IT is an increasingly hybrid, distributed-but-integrated, and complex endeavor, both on and off premises; with burgeoning scale, applications, data, devices … and vendors,” wrote Mark Peters, senior analyst at ESG in a blog post. “Modern applications frustrate most legacy monitoring solutions as they are ephemeral, with services being spun up and down in mere seconds, which makes it difficult for solutions that capture data at every 15, 5, or even 1-minute intervals.”

Peters added that the Oracle platform is a “pretty sexy answer to this long-term vexing issue. It gives organizations a level of ‘arms wrapped around everything’ control, based upon its single source of truth and comprehensive visibility.”

Outside of Oracle’s claimed performance advantage over cloud rivals AWS and Microsoft, a number of other vendors offer similar multi-cloud management tools. VMware, for instance, offers its CloudHealth product that it gained from acquiring CloudHealth in 2018.

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Microsoft will launch a raft of new features for its Azure Stream Analytics platform in preview next week, though you’ll have to wait at least a few weeks for general availability.

Microsoft pitches Azure Stream Analytics as a platform for quickly developing and deploying complex serverless analytics on data streams.

Top of the list of new features is online scaling, which means users no longer have to stop and restart a Stream Analytics job to change the number of Streaming Units allocated to a job. Streaming units represent the amount of compute resources allocated to a job.

As Microsoft puts it, “This builds on the customer promise of long-running mission-critical pipelines that Stream Analytics offers today.”

Also new is the ability to implement custom deserializers in C#, “which can then be used to de-serialize events received by Azure Stream Analytics.” At the same time, developers can now create Stream Analytics modules that write or reuse custom C# functions and invoke them right in the query through User Defined Functions. Both of these changes will give developers more to play with when dealing with IoT or other edge applications.

Other new features include the ability to debug query steps in Visual Studio, and to carry out local testing on live data in Visual Studio code, and the option of of managed identity authentication via PowerBI.

The public preview of all these features will open on November 4, with general availability coming some weeks later.

Looking a little further ahead, Microsoft has pencilled in a public preview of the ability to analyze ingress data from Event Hubs or IoT Hub on Azure Stack, and egress the results to a blob storage or SQL database  in January 2020. 

And if you like playing secret squirrel, you might like to know the company is soliciting signups for a Private Preview for real time scoring courtesy of Azure Machine Learning. The service will be based on custom pre-trained models managed by Azure Machine Learning, and hosted in either Azure Kubernetes Service or Azure Container Instances. Models can be built with a range of Python libraries, including Scikit-learn, PyTorch, TensorFlow, and trained on platforms, including Azure Databricks, Azure Machine Learning Compute, and HD Insight.

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At several points throughout his career, Phil Calçado has worked on transitioning monoliths to a microservices architecture, but recently the challenge for him has instead been in migrating to serverless. In a presentation at the recent QCon New York conference, he talked about his experience combining the serverless concept with microservices.

Calçado, previously having worked for Meetup and SoundCloud, and currently at SeatGeek, started by defining microservices as being highly distributed application architectures, and emphasizes distributed application with a focus on business logic, in contrast to the commonly used term distributed system, where he believes there is too much focus on infrastructure.

From his microservices perspective, Calçado finds it hard to define serverless and notes that people seem to have very different definitions. He therefore refers to the book What is Serverless? written by Mike Roberts and John Chapin, both at Symphonia. One distinctive feature of serverless, he points out, is that there is a disconnect between the code you work with and the concept of service, which means that you don’t think about CPU, memory, and so on.

Meetup is one of the original New York City startups and is mostly based on one big monolithic JVM-based application, which is hard to work with. In an attempt to move towards serverless, they started a project aiming for a new asynchronous event-based design. In the new design, events are created from changes in the database made by the monolith, and these events are then stored and processed by lambda functions and finally stored as specific views (CQRS projections) needed by the business.

The project was not so successful; they reverted their approach and went back to using an API. One problem that often occurred was that solving a bug also meant reprocessing all data, since the code change could mean a change in logic and incorrect data in the views. Another problem was how to handle writes back to the monolith. This ultimately was too complicated, so they took a pragmatic approach and used the legacy code as an API for writes. But this solution raised the question if all the work with events and lambdas when reading data was necessary. The biggest problem howver, was the lack of governance; they had no control of ownership, where functions were deployed, which ones that were in production, and other similar problems.

Calçado notes though that some things worked out much better than he expected. Getting new engineers to be productive went faster than expected. Having the engineering teams both own and operate their own deployments worked much better than he had experienced before, and he believes one reason for this is the small size of lambdas, which makes it easier to have full control. Developer happiness was only higher than he expected.

A problem Calçado sees with serverless is that with microservices, we still have a fairly limited number of small services, but with serverless the features are split into a huge number of functions. Often these functions also communicate with other functions in a form of peer-to-peer network, which makes it very hard to know what’s happening anymore. He refers to a tweet by Chris Ford and calls this: Pinball machine architecture.

To avoid this type of architecture, he refers to Martin Fowler and his article from 2002: Public versus Published Interfaces. Although there are interfaces that are public, they still should not be used. The provider of a library or similar type of software should provide a published interface, which is the only one that should be used. Based on this idea, Calçado and his teams separated all functions into groups with an API gateway in front of each group, and the gateways are the only way of accessing the functions. He admits that this is like creating services with functions as the computing units — using serverless as building blocks for microservices.

Calçado notes that at Meetup they went full on AWS, but even though they did everything the Amazon way, he still had about 10% of the engineering teams dedicated to tooling and platforms. There are still a lot of loose ends with things that don’t communicate with each other, and other similar problems.

When looking back at what they have achieved, Calçado is not sure if the result really is serverless, but emphasizes that the important thing is that they have managed to take a couple of steps from a big and complicated legacy application, towards a world of serverless computing. He notes that although serverless looks like the future, we are not there yet. But with better tooling, he believes it’s the right way to go.

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