Amazon EC2 Options

With Amazon EC2, you pay only for the compute time that you use. Amazon EC2 offers a variety of pricing options for different use cases. For example, if your use case can withstand interruptions, you can save with Spot Instances. You can also save by committing early and locking in a minimum level of use with Reserved Instances.

On-Demand

are ideal for short-term, irregular workloads that cannot be interrupted. No upfront costs or minimum contracts apply. The instances run continuously until you stop them, and you pay for only the compute time you use.

Sample use cases for On-Demand Instances include developing and testing applications and running applications that have unpredictable usage patterns. On-Demand Instances are not recommended for workloads that last a year or longer because these workloads can experience greater cost savings using Reserved Instances.

Amazon EC2 Savings Plans

AWS offers Savings Plans for several compute services, including Amazon EC2. Amazon EC2 Savings Plans enable you to reduce your compute costs by committing to a consistent amount of compute usage for a 1-year or 3-year term. This term commitment results in savings of up to 66% over On-Demand costs.

Any usage up to the commitment is charged at the discounted plan rate (for example, $10 an hour). Any usage beyond the commitment is charged at regular On-Demand rates.

Later in this course, you will review AWS Cost Explorer, a tool that enables you to visualize, understand, and manage your AWS costs and usage over time. If you are considering your options for Savings Plans, AWS Cost Explorer can analyze your Amazon EC2 usage over the past 7, 30, or 60 days. AWS Cost Explorer also provides customized recommendations for Savings Plans. These recommendations estimate how much you could save on your monthly Amazon EC2 costs, based on previous Amazon EC2 usage and the hourly commitment amount in a 1-year or 3-year plan.

Reserved Instances

are a billing discount applied to the use of On-Demand Instances in your account. You can purchase Standard Reserved and Convertible Reserved Instances for a 1-year or 3-year term, and Scheduled Reserved Instances for a 1-year term. You realize greater cost savings with the 3-year option.

At the end of a Reserved Instance term, you can continue using the Amazon EC2 instance without interruption. However, you are charged On-Demand rates until you do one of the following:

  • Terminate the instance.
  • Purchase a new Reserved Instance that matches the instance attributes (instance type, Region, tenancy, and platform).

Spot Instances

 are ideal for workloads with flexible start and end times, or that can withstand interruptions. Spot Instances use unused Amazon EC2 computing capacity and offer you cost savings at up to 90% off of On-Demand prices.

Suppose that you have a background processing job that can start and stop as needed (such as the data processing job for a customer survey). You want to start and stop the processing job without affecting the overall operations of your business. If you make a Spot request and Amazon EC2 capacity is available, your Spot Instance launches. However, if you make a Spot request and Amazon EC2 capacity is unavailable, the request is not successful until capacity becomes available. The unavailable capacity might delay the launch of your background processing job.

After you have launched a Spot Instance, if capacity is no longer available or demand for Spot Instances increases, your instance may be interrupted. This might not pose any issues for your background processing job. However, in the earlier example of developing and testing applications, you would most likely want to avoid unexpected interruptions. Therefore, choose a different EC2 instance type that is ideal for those tasks.

Dedicated Hosts

are physical servers with Amazon EC2 instance capacity that is fully dedicated to your use. 

You can use your existing per-socket, per-core, or per-VM software licenses to help maintain license compliance. You can purchase On-Demand Dedicated Hosts and Dedicated Hosts Reservations. Of all the Amazon EC2 options that were covered, Dedicated Hosts are the most expensive.

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Osama

Amazon EC2 instance types

Amazon EC2 instance types are optimized for different tasks. When selecting an instance type, consider the specific needs of your workloads and applications. This might include requirements for compute, memory, or storage capabilities.

General purpose instances

provide a balance of compute, memory, and networking resources. You can use them for a variety of workloads, such as:

  • application servers
  • gaming servers
  • backend servers for enterprise applications
  • small and medium databases

Suppose that you have an application in which the resource needs for compute, memory, and networking are roughly equivalent. You might consider running it on a general purpose instance because the application does not require optimization in any single resource area.

Compute optimized instances

are ideal for compute-bound applications that benefit from high-performance processors. Like general purpose instances, you can use compute optimized instances for workloads such as web, application, and gaming servers.

However, the difference is compute optimized applications are ideal for high-performance web servers, compute-intensive applications servers, and dedicated gaming servers. You can also use compute optimized instances for batch processing workloads that require processing many transactions in a single group.

Memory optimized instances

are designed to deliver fast performance for workloads that process large datasets in memory. In computing, memory is a temporary storage area. It holds all the data and instructions that a central processing unit (CPU) needs to be able to complete actions. Before a computer program or application is able to run, it is loaded from storage into memory. This preloading process gives the CPU direct access to the computer program.

Suppose that you have a workload that requires large amounts of data to be preloaded before running an application. This scenario might be a high-performance database or a workload that involves performing real-time processing of a large amount of unstructured data. In these types of use cases, consider using a memory optimized instance. Memory optimized instances enable you to run workloads with high memory needs and receive great performance.

Accelerated computing instances

use hardware accelerators, or coprocessors, to perform some functions more efficiently than is possible in software running on CPUs. Examples of these functions include floating-point number calculations, graphics processing, and data pattern matching.

In computing, a hardware accelerator is a component that can expedite data processing. Accelerated computing instances are ideal for workloads such as graphics applications, game streaming, and application streaming.

Storage optimized instances

are designed for workloads that require high, sequential read and write access to large datasets on local storage. Examples of workloads suitable for storage optimized instances include distributed file systems, data warehousing applications, and high-frequency online transaction processing (OLTP) systems.

In computing, the term input/output operations per second (IOPS) is a metric that measures the performance of a storage device. It indicates how many different input or output operations a device can perform in one second. Storage optimized instances are designed to deliver tens of thousands of low-latency, random IOPS to applications. 

You can think of input operations as data put into a system, such as records entered into a database. An output operation is data generated by a server. An example of output might be the analytics performed on the records in a database. If you have an application that has a high IOPS requirement, a storage optimized instance can provide better performance over other instance types not optimized for this kind of use case.

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Osama

OCI Basics – Putting Data into Object Storage OCI

The Object Storage service provides reliable, secure, and scalable object storage. Object storage is a storage architecture that stores and manages data as objects. Some typical use cases include data backup, file sharing, and storing unstructured data like logs and sensor-generated data.

Creating a Bucket

  1. Open the navigation menu and click Storage. Under Object Storage, click Buckets.A list of the buckets in the compartment you’re viewing is displayed.
  2. Select a compartment from the Compartment list on the left side of the page.A list of existing buckets is displayed.
  3. Click Create Bucket.
    • Bucket Name
    • Default Storage Tier: Select the default tier in which you want to store your data
      • Standard is the primary, default storage tier Use the Standard tier for storing frequently accessed data that requires fast and immediate access.
      • Archive is the default storage tier used for archive storage, Use the Archive tier for storing rarely accessed data that requires long retention periods. Access to data in the Archive tier is not immediate. Archived data must be restored before the data is accessible.
      • Object Events: Select Emit Object Events if you want to enable the bucket to emit events for object state changes. For more information about events.
      • Encryption: Buckets are encrypted with keys managed by Oracle by default, but you can optionally encrypt the data in this bucket using your own Vault encryption key. To use Vault for your encryption needs, select Encrypt Using Customer-Managed Keys

Uploading Files to a Bucket

To upload files to your bucket using the Console:

  1. From the Object Storage Buckets screen, click the bucket name to view its details.
  2. Click Upload.
  3. In the Object Name Prefix field, optionally specify a file name prefix for the files that you plan to upload.
  4. If the Storage Tier field displays Standard, you can optionally change the storage tier to upload objects to.

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Osama

Launching Windows Instance on OCI

In this post  I will show you how to launch and connect to a Windows instance.

  • Create a cloud network and subnet that enables internet access
  • Launch an instance
  • Connect to the instance
  • Add and attach a block volume

I already posted a post how to Launch Linux Instance on OCI here, in the post you will have to follow the first two steps which is creating

  • Choose a compartment for your resources.
  • Create a cloud network.

Once you are done, you can start with steps #3 which will allow you to launch a instance – windows one.

  1. Open the navigation menu and click Compute. Under Compute, click Instances.
  2. Click Create instance.
  3. In the Placement section, accept the default Availability domain.
  4. In the Image and shape section, do the following:
    • In the Image source list, select Platform images.
    • Select Windows. Then, in the OS version list, select Server 2019 Standard.
    • Review and accept the terms of use, and then click Select image.
  5. In the Shape section, click Change Shape. Then, do the following:
    • For Instance type, accept the default, Virtual machine.
    • For Shape series, select AMD, and then choose either the VM.Standard.E4.Flex shape or the VM.Standard.E3.Flex shape (it doesn’t matter which). Accept the default values for OCPUs and memory.
    • The shape defines the number of CPUs and amount of memory allocated to the instance.
  6. In the Networking section, configure the network details for the instance. Do not accept the defaults.
    • For Primary network, leave Select existing virtual cloud network selected.
    • Select the cloud network that you created. If necessary, click Change Compartment to switch to the compartment containing the cloud network that you created.
  7. In the Boot volume section, leave all the options cleared.

Your instance now is ready.

Connect to the windows instance done by using Remote desktop, enter the public ip, username which is (opc), and the password.

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Osama

Deployment strategies

All-at-once

All-at-once deployments instantly shift traffic from the original (old) Lambda function to the updated (new) Lambda function, all at one time. All-at-once deployments can be beneficial when the speed of your deployments matters. In this strategy, the new version of your code is released quickly, and all your users get to access it immediately.

Canary

A linear deployment is similar to canary deployment. In this strategy, you direct a small amount of traffic to

In a canary deployment, you deploy your new version of your application code and shift a small percentage of production traffic to point to that new version. After you have validated that this version is safe and not causing errors, you direct all traffic to the new version of your code.

Linear

A linear deployment is similar to canary deployment. In this strategy, you direct a small amount of traffic to your new version of code at first. After a specified period of time, you automatically increment the amount of traffic that you send to the new version until you’re sending 100% of production traffic.

Comparing deployment strategies

To help you decide which deployment strategy to use for your application, you’ll need to consider each option’s consumer impact, rollback, event model factors, and deployment speed. The comparison table below illustrates these points.

DeploymentConsumer ImpactRollbackEvent Model FactorsDeployment Speed
All-at-onceAll at onceRedeploy older versionAny event model at low concurrency rateImmediate 
Canary/
Linear
1-10% typical initial traffic shift, then phased Revert 100% of traffic to previous deploymentBetter for high-concurrency workloadsMinutes to hours

Deployment preferences with AWS SAM

Traffic shifting with aliases is directly integrated into AWS SAM. If you’d like to use all-at-once, canary, or linear deployments with your Lambda functions, you can embed that directly into your AWS SAM templates. You can do this in the deployment preferences section of the template. AWS CodeDeploy uses the deployment preferences section to manage the function rollout as part of the AWS CloudFormation stack update. SAM has several pre-built deployment preferences you can use to deploy your code. See the table below for examples. 

Deployment Preferences TypeDescription
Canary10Percent30MinutesShifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 30 minutes later.
Canary10Percent5MinutesShifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 5 minutes later.
Canary10Percent10MinutesShifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 10 minutes later.
Canary10Percent15MinutesShifts 10 percent of traffic in the first increment. The remaining 90 percent is deployed 15 minutes later.
Linear10PercentEvery10MinutesShifts 10 percent of traffic every 10 minutes until all traffic is shifted.
Linear10PercentEvery1MinuteShifts 10 percent of traffic every minute until all traffic is shifted.
Linear10PercentEvery2MinutesShifts 10 percent of traffic every 2 minutes until all traffic is shifted.
Linear10PercentEvery3MinutesShifts 10 percent of traffic every 3 minutes until all traffic is shifted.
AllAtOnceShifts all traffic to the updated Lambda functions at once.

Creating a deployment pipeline

When you check a piece of code into source control, you don’t want to wait for a human to manually approve it or have each piece of code run through different quality checks. Using a CI/CD pipeline can help automate the steps required to release your software deployment and standardize on a core set of quality checks.

Reference

Redeploy and roll back a deployment with CodeDeploy

What is AWS Lambda?

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Osama

CloudWatch metrics vs AWS X-RAY

Review the built-in Amazon CloudWatch metrics and their dimensions for each of the services you plan to use so that you can decide how to best leverage them vs. adding custom metrics. There are also many third-party tools that provide monitoring and metrics reporting from CloudWatch data.

Business metrics

Business Key Performance Indicators (KPIs) measure your application performance against business goals. It is extremely important to know when something is critically affecting your overall business (revenue wise or not).

Examples: Orders placed, debit/credit card operations, flights purchased

Customer experience metrics

Customer experience data dictates not only the overall effectiveness of the UI/UX but also whether changes or anomalies are affecting the customer experience in a particular section of your application. These metrics are often measured in percentiles to prevent outliers when trying to understand the impact over time and how widespread it is across your customer base.

Examples: Perceived latency, time it takes to add an item to a basket/to checkout, page load times

System metrics

Vendor and application metrics are important to underpin root causes. System metrics also tell you if your systems are healthy, at risk, or already impacting your customers.

Examples: Percentage of HTTP errors/success, memory utilization, function duration/error/throttling, queue length, stream records length, integration latency

Operational metrics

Ops metrics are important to understand sustainability and maintenance of a given system and crucial to pinpoint how stability has progressed/degraded over time.

Examples: Number of tickets([un]successful resolutions, etc.), number of times people on-call were paged, availability, CI/CD pipeline stats (successful/failed deployments, feedback time, cycle and lead time)

Built-in CloudWatch metrics pages

CloudWatch Logs

Logs let you dig into specific issues, but you can also use log data to create business-level metrics via CloudWatch Logs metric filters. You can interact with logs via CloudWatch Logs to drill into any specific log entry or filter them based on a pattern to create your own metrics. See how the services listed below interact with CloudWatch Logs.

Lambda logs

Lambda automatically logs all requests handled by your function and stores them in CloudWatch Logs. This gives you access to information about each invocation of your Lambda function.

You can log almost anything to CloudWatch Logs by using print or standard out statements in your functions. When you create custom logs, use a structured format like a JSON event to make it easier to report from them.

API Gateway execution and access logs

API Gateway execution logs include information on errors as well as execution traces. Info like parameter values, payload, Lambda authorizers used, and API keys appear in the execution logs. You can log just errors or errors and info. Logging is set up per API stage. These logs are detailed, so you want to be thoughtful about what you need. Also, log groups don’t expire by default, so make sure to set retention values suitable to your workload.

You can also create custom access logs and send them to your preferred CloudWatch group to track who is accessing your APIs and how. You can specify the access details by selecting context variables and choosing the format you want to use.

CloudWatch Logs Insights

CloudWatch Log Insights lets you use prebuilt or custom queries on your logs to provide aggregated views and reporting.  If you’ve created structured custom logs, CloudWatch Logs Insights can automatically discover the fields in your logs to make it easy to query and group your log data.

X-RAY

When a transaction fails, or completes slower than expected, how do you figure out where in the flow of services it failed? X-Ray gives you a visual representation of your services—a service map—that illustrates each integration point, and gives you quick insight into successes and failures. Then, you can drill down into the details of each individual trace. 

You can enable X-Ray with one click for Lambda, API Gateway, and Amazon SNS. You can also turn it on for SQS queues that are not Lambda event sources, and you can add custom instrumentation to your function using the X-Ray SDK to write your own code. X-Ray integrations support both active and passive instrumentation. 

-Ray instrumentation

You can add custom instrumentation to your function using the X-Ray SDK to write your own code. X-Ray integrations support both active and passive instrumentation:

Service IntegrationsActive InstrumentationPassive Instrumentation
Samples and instruments incoming requestsInstruments requests that have been sampled by another service
Writes traces to X-RayCan add information to traces
AWS Lambda✔️✔️
Amazon API Gateway✔️✔️
Amazon SNS ✔️
Amazon SQS✔️
  • CloudWatch metrics – To view how resources are performing, CloudWatch metrics is the best solution. If a developer needs to check how many times a Lambda function has been invoked,
  • CloudWatch Logs Insights – CloudWatch Logs Insights enables you to interactively query your log data in CloudWatch Logs. If a team wants to search and query their logs for their API, CloudWatch Logs Insights would be the best option.
  • CloudWatch Logs – You can insert logging statements into your code to help you validate that your code is working as expected. Lambda automatically integrates with CloudWatch Logs and pushes all logs from your code to CloudWatch. If an engineer wants to see what parameters are being passed into a function, they can insert logging statements in the code and check the response in CloudWatch Logs.
  • X-Ray – X-Ray provides a visual map of successes and failures and lets you drill into individual traces for an execution and drill down into the details of how long each leg of the execution took.

AWS CloudTrail

  • Records IAM user, IAM role, and AWS service API activity in your account.
  • Is enabled when you create an account.
  • Provides full details about the API action, like identity of the requestor, time of the API call, request parameters, and response elements returned by the service.
CloudTrail event

When activity occurs in your AWS account, that activity is recorded in a CloudTrail event, and you can see recent events in the event history.

The CloudTrail event history provides a viewable, searchable, and downloadable record of the past 90 days of CloudTrail events. Use this history to gain visibility into actions taken in your AWS account in the AWS Management Console, AWS SDKs, command line tools, and other AWS services.

CloudTrail Trails

A trail is a configuration that enables delivery of CloudTrail events to an Amazon S3 bucket, CloudWatch Logs, and CloudWatch Events. If you need to maintain a longer history of events, you can create your own trail. When you create a trail, it tracks events performed on or within resources in your AWS account and writes them to an S3 bucket you specify.

For example, a trail could capture modifications to your API Gateway APIs. You can optionally add data events to track S3 object-level API activity (like when someone uploads something to the bucket) or Lambda invoke API operations on one or all future Lambda functions in the account.

You can configure CloudTrail Insights on your trails to help you identify and respond to unusual activity associated with write API calls. CloudTrail Insights is a feature that tracks your normal patterns of API call volume and generates Insights events when the volume is outside normal patterns.  

Reference

CloudTrail concepts

Sending CloudTrail Events to CloudWatch Logs

AWS Config Concepts

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Osama

Migrating to Serverless

we’ll look at considerations for migrating existing applications to serverless and common ways for extending the serverless

At a high level, there are three migration patterns that you might follow to migrate your legacy your applications to a serverless model.

Leapfrog

As the name suggests, you bypass interim steps and go straight from an on-premises legacy architecture to a serverless cloud architecture

Organic

You move on-premises applications to the cloud in more of a “lift and shift” model. In this model, existing applications are kept intact, either running on Amazon Elastic Compute Cloud (Amazon EC2) instances or with some limited rewrites to container services like Amazon Elastic Kubernetes Service (Amazon EKS)/Amazon Elastic Container Service (Amazon ECS) or AWS Fargate.

Developers experiment with Lambda in low-risk internal scenarios like log processing or cron jobs. As you gain more experience, you might use serverless components for tasks like data transformations and parallelization of processes.

At some point in the adoption curve, you take a more strategic look at how serverless and microservices might address business goals like market agility, developer innovation, and total cost of ownership.

You get buy-in for a more long-term commitment to invest in modernizing your applications and select a production workload as a pilot. With initial success and lessons learned, adoption accelerates, and more applications are migrated to microservices and serverless.

Strangler

With the strangler pattern, an organization incrementally and systematically decomposes monolithic applications by creating APIs and building event-driven components that gradually replace components of the legacy application.

Distinct API endpoints can point to old vs. new components, and safe deployment options (like canary deployments) let you point back to the legacy version with very little risk.

New feature branches can be “serverless first,” and legacy components can be decommissioned as they are replaced. This pattern represents a more systematic approach to adopting serverless, allowing you to move to critical improvements where you see benefit quickly but with less risk and upheaval than the leapfrog pattern.

Migration questions to answer:

  • What does this application do, and how are its components organized?
  • How can you break your data needs up based on the command query responsibility (CQRS) pattern?
  • How does the application scale, and what components drive the capacity you need?
  • Do you have schedule-based tasks?
  • Do you have workers listening to a queue?
  • Where can you refactor or enhance functionality without impacting the current implementation?

Application Load Balancer vs. API Gateway for directing traffic to serverless targets

Application Load BalancerAmazon API Gateway
Easier to transition existing compute stack where you are already using an Application Load BalancerGood for building REST APIs and integrating with other services and Lambda functions
Supports authorization via OIDC-capable providers, including Amazon Cognito user poolsSupports authorization via AWS Identity and Access Management (IAM), Amazon Cognito, and Lambda authorizers
Charged by the hour, based on Load Balancer Capacity UnitsCharged based on requests served
May be more cost-effective for a steady stream of trafficMay be more cost-effective for spiky patterns
Additional features for API management: 
Export SDK for clients
Use throttling and usage plans to control access
Maintain multiple versions of an APICanary deployments

Consider three factors when comparing costs of ownership:

  • The infrastructure cost to run your workload (for example, the costs for your provisioned EC2 capacity vs. the per-invocation cost of your Lambda functions)
  • The development effort to plan, architect, and provision resources on which the application will run
  • The costs of your team’s time to maintain the application once it is in production

Reference

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Osama

AWS Transit Gateway

AWS Transit Gateway is a highly available and scalable service that provides interconnectivity between VPCs and your on-premises network. Within a Region, AWS Transit Gateway provides a method for consolidating and centrally managing routing between VPCs with a hub-and-spoke network architecture.

Between Regions, AWS Transit Gateway supports inter-regional peering with other transit gateways. It does this to facilitate routing network traffic between VPCs of different Regions over the AWS global backbone. This removes the need to route traffic over the internet. AWS Transit Gateway also integrates with hybrid network configurations when a Direct Connect or AWS Site-to-Site VPN connection is connected to the transit gateway.

AWS Transit Gateway concepts

Attachments

AWS Transit Gateway supports the following connections: 

  • One or more VPCs
  • A compatible Software-Defined Wide Area Network (SD-WAN) appliance
  • A Direct Connect gateway
  • A peering connection with another transit gateway
  • A VPN connection to a transit gateway

AWS Transit Gateway MTU

AWS Transit Gateway supports an MTU of 8,500 bytes for:

  • VPC connections
  • Direct Connect connections
  • Connections to other transit gateways
  • Peering connections

AWS Transit Gateway supports an MTU of 1,500 bytes for VPN connections.

AWS Transit Gateway route table

A transit gateway has a default route table and can optionally have additional route tables. A route table includes dynamic and static routes that decide the next hop based on the destination IP address of the packet. The target of these routes can be any transit gateway attachment. 

Associations

Each attachment is associated with exactly one route table. Each route table can be associated with zero to many attachments.

Route propagation

A VPC, VPN connection, or Direct Connect gateway can dynamically propagate routes to a transit gateway route table. With a Direct Connect attachment, the routes are propagated to a transit gateway route table by default.

With a VPC, you must create static routes to send traffic to the transit gateway.


With a VPN connection or a Direct Connect gateway, routes are propagated from the transit gateway to your on-premises router using BGP.

With a peering attachment, you must create a static route in the transit gateway route table to point to the peering attachment.

AWS Transit Gateway inter-regional peering

AWS offers two types of peering connections for routing traffic between VPCs in different Regions: VPC peering and transit gateway peering. Both peering types are one-to-one, but transit gateway peering connections have a simpler network design and more consolidated management. 

Suppose a customer has multiple VPCs in three different Regions. As the following diagram illustrates, to permit network traffic to route between each VPC requires creating 72 VPC peering connections. Each VPC needs 8 different routing configurations and security policies. 

With AWS Transit Gateway, the same environment only needs three peering connections. The transit gateway in each Region facilitates routing network traffic to all the VPCs in its Region. Because all routing can be managed by the transit gateway, the customer only needs to maintain three routing configurations, simplifying management.

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Osama

Use case for AWS database

Amazon Simple Storage Service (Amazon S3)

  • Data lakes
  • Economical state store
  • Claim check pattern
  • Filter data retrieved by Lambda (S3 Select)

Amazon DynamoDB

  • Key-value data store with millisecond response time
  • Capture changes with DynamoDB Streams and index to other data stores

Amazon ElastiCache for Redis

  • Well suited for things like real-time leaderboards
  • In-memory data store with sub-millisecond read and write latency

Amazon Quantum Ledger Database (Amazon QLDB)

  • Model state changes in a cryptographically provable manner
  • Distributed ledger

Amazon Aurora

  • High-volume, high-throughput, and highly parallelized transactional data
  • MySQL- and PostgreSQL-compatible relational database built for faster performance at lower costs
  • Aurora Serverless automatically starts up, scales, and shuts down based on traffic

Amazon Relational Database Service (Amazon RDS)

  • Run familiar database engines with less administration

  • Amazon DynamoDB – Store player game state for 300 million users while maintaining millisecond access
  • Amazon QLDB – Track the history of credits and debits in banking transactions in an organization
  • Amazon ElastiCache for Redis – A sorted leaderboard of top values requiring sub-millisecond read and write latencies
  • Amazon RDS – A relational data store to back a CMS that reduces operational overhead
  • Amazon S3 – Ideal for data lakes, due to its high scalability and flexibility

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Osama

AWS Site-to-Site VPN and AWS Client VPN

AWS VPN is comprised of two services: 

  • AWS Site-to-Site VPN enables you to securely connect your on-premises network to Amazon VPC, for example your branch office site. 
  • AWS Client VPN enables you to securely connect users to AWS or on-premises networks, for example remote employees. 

AWS Site-to-Site VPN

ased on IPsec technology, AWS Site-to-Site VPN uses a VPN tunnel to pass data from the customer network to or from AWS.

One AWS Site-to-Site VPN connection consists of two tunnels. Each tunnel terminates in a different Availability Zone on the AWS side, but it must terminate on the same customer gateway on the customer side. 

AWS Site-to-Site VPN components

Customer gateway

A resource you create and configure in AWS that represents your on-premise gateway device. The resource contains information about the type of routing used by the Site-to-Site VPN, BGP, ASN and other optional configuration information.

Customer gateway device

A customer gateway device is a physical device or software application on your side of the AWS Site-to-Site VPN connection. 

Virtual private gateway

A virtual private gateway is the VPN concentrator on the Amazon side of the AWS Site-to-Site VPN connection. You use a virtual private gateway or a transit gateway as the gateway for the Amazon side of the AWS Site-to-Site VPN connection.

Transit gateway

A transit gateway is a transit hub that can be used to interconnect your VPCs and on-premises networks. You use a transit gateway or virtual private gateway as the gateway for the Amazon side of the AWS Site-to-Site VPN connection.

AWS Site-to-Site VPN limitations

  • IPv6 traffic is partially supported. AWS Site-to-Site VPN supports IPv4/IPv6-Dualstack through separate tunnels for inner traffic. IPv6 for outer tunnel connection not supported.
  • AWS Site-to-Site VPN does not support Path MTU Discovery. The greatest Maximum Transmission Unit (MTU) available on the inside tunnel interface is 1,399 bytes.
  • Throughput of AWS Site-to-Site VPN connections is limited. When terminating on a virtual private gateway, only one tunnel out of the pair can be active and carry a maximum of 1.25 Gbps. However, real-life throughput will be about 1 Gbps. When terminating on AWS Transit Gateway, both tunnels in the pair can be active and carry an aggregate maximum of 2.5 Gbps. However, real-life throughput will be 2 Gbps. Each flow (for example, TCP stream) will still be limited to a maximum of 1.25 Gbps, with a real-life value of about 1 Gbps.
  • Maximum packets per second (PPS) per VPN tunnel is 140,000.
  • AWS Site-to-Site VPN terminating on AWS Transit Gateway supports equal-cost multi-path routing (ECMP) and multi-exit discriminator (MED) across tunnels in the same and different connection. ECMP is only supported for Site-to-Site VPN connections activated on an AWS Transit Gateway. MED is used to identify the primary tunnel for Site-to-Site VPN conncetions that use BGP. Note, BFD is not yet supported on AWS Site-to-Site VPN, though it is supported on Direct Connect. 
  • AWS Site-to-Site VPN endpoints use public IPv4 addresses and therefore require a public virtual interface to transport traffic over Direct Connect. Support for AWS Site-to-Site VPN over private Direct Connect is not yet available. 
  • For globally distributed applications, the accelerated Site-to-Site VPN option provides a connection to the global AWS backbone through AWS Global Accelerator. Because the Global Accelerator IP space is not announced over a Direct Connect public virtual interface, you cannot use accelerated Site-to-Site VPN with a Direct Connect public virtual interface.

In addition, when you connect your VPCs to a common on-premises network, it’s recommend that you use nonoverlapping CIDR blocks for your networks. 

Client VPN

Based on OpenVPN technology, Client VPN is a managed client-based VPN service that lets you securely access your AWS resources and resources in your on-premises network. With Client VPN, you can access your resources from any location using an OpenVPN-based VPN client. 

Client VPN components

Client VPN endpoint

Your Client VPN administrator creates and configures a Client VPN endpoint in AWS. Your administrator controls which networks and resources you can access when you establish a VPN connection. 

VPN client application

This is the software application that you use to connect to the Client VPN endpoint and establish a secure VPN connection.

Client VPN endpoint configuration file

This is a configuration file that is provided to you by your Client VPN administrator. The file includes information about the Client VPN endpoint and the certificates required to establish a VPN connection. You load this file into your chosen VPN client application. 

Client VPN limitations

  • Client VPN supports IPv4 traffic only. IPv6 is not supported.
  • Security Assertion Markup Language (SAML) 2.0-based federated authentication only works with an AWS provided client v1.2.0 or later. 
  • SAML integration with AWS Single Sign-On requires a workaround. Better integration is being worked on. 
  • Client CIDR ranges must have a block size of at least /22 and must not be greater than /12. 
  • A Client VPN endpoint does not support subnet associations in a dedicated tenancy VPC. 
  • Client VPN is not compliant with Federal Information Processing Standards (FIPS).
  • Client CIDR ranges cannot overlap with the local CIDR of the VPC in which the associated subnet is located. It also cannot overlap any routes manually added to the Client VPN endpoint’s route table.
  • A portion of the addresses in the client CIDR range is used to support the availability model of the Client VPN endpoint and cannot be assigned to clients. Therefore, we recommend that you assign a CIDR block that contains twice the number of required IP addresses. This will ensure the maximum number of concurrent connections that you plan to support on the Client VPN endpoint. 
  • The client CIDR range cannot be changed after you create the Client VPN endpoint. 
  • The subnets associated with a Client VPN endpoint must be in the same VPC.
  • You cannot associate multiple subnets from the same Availability Zone with a Client VPN endpoint. 
  • AWS Certificate Manager (ACM) certificates are not supported with mutual authentication because you cannot extract the private key. You can use an ACM server as the server-side certificate. But, to add a client certificate to your customer configuration, you cannot use a general ACM certificate because you can’t extract the required private key details. So you must access the keys in one of two ways. Either generate your own certificate where you have the key or use AWS Certificate Manager Private Certificate Authority (ACM PCA), which gives the private keys. If the customer is authenticating based on Active Directory or SAML, they can use a general ACM-generated certificate because only the server certificate is required.

Cheers
Osama