#AWSManagementConsole

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IaC Generator To Import SCPs And RCPs Into CloudFormation

Import SCPs and RCPs from current AWS organisations into Cloud Formation architecture as a code generator. AWS Organisations customers often manually set up resource control policies (RCPs) and service control policies (SCPs) using the AWS Management Console or AWS CLI.

This manual process may become onerous as the company grows and adds policies. It may limit visibility into all SCPs and RCPs, their targets, and update efficiency. Without visibility and access limits, it's hard to track who's making changes and how. CloudFormation can simplify rule management with rollback, policy validation via Hooks, and history preserving. Git sync may also synchronise stacks with Git repository source code. Git sync lets you deploy, configure, and update CloudFormation stacks using pull requests and version tracking. When you alter the deployment file or template, CloudFormation updates the stack.

CloudFormation IaC generator

This article shows how to import SCPs and RCPs into AWS CloudFormation templates using the CloudFormation infrastructure as a code generator (IaC generator). The IaC generator automates SCP and RCP administration at scale. Important: CloudFormation imports current policies, not recreates them.

Solution overview This article provides a command-line tool to locate SCPs and RCPs in your firm and automate policy import into CloudFormation templates.

The end-to-end flow is shown in the previous graphic:

Start the tool: Both the management account and the administrator account can run the software, automating following steps. Determine corporate SCPs and RCPs: The tool initially requests policies from the Organisations service via API. Total SCPs and RCPs are tallied. Determine AWS Control Tower RCPs, SCPs, and targetless policies: The utility finds AWS Control Tower SCPs and RCPs and outputs a list. Their policy titles begin with “aws-guardrails-” to identify SCPs. AWS Control Tower-Controls- prefixes RCP policy names identify them. Policies without targets: The tool lists SCPs and RCPs without accounts, roots, or OUs. These policies may be duplicates or reallocated. IaC generator scan: You will be asked if you want to import policies into CloudFormation templates using the resource scan. If you click “yes,” the tool will utilise the IaC generator to launch a CloudFormation resource scan to retrieve policy name, targets, tags, etc. Use scanned policy resources to construct a template: The program creates CloudFormation templates from policy resources. Any policies without objectives will be in the template. Review procedure: Use the CloudFormation IaC generator to preview the template after generation. Create CloudFormation stacks using templates: After reviewing templates, import them into CloudFormation stacks to deploy. Remember that CloudFormation policies are imported, not rebuilt. Templates reflect current policies and qualities.

Thinking before applying the solution

Consider these factors before applying the solution.

If you have enabled policy management delegation for AWS Organisations, execute this solution with the delegated administrator account. If not, execute the solution using the management account. Note: Delegated administrator member accounts should handle organisation policies. The CloudFormation templates will not import SCPs and RCPs (with or without targets) since they should be managed by AWS Control Tower. Changes to AWS Control Tower resources outside of AWS Control Tower might cause drift and unanticipated effects. Fully access SCP and RCP on AWSCloudFormation stacks cannot import AWS managed policies like FullAWSAccess RCP. If you surpass CloudFormation template size limitations, several templates may be produced. The program automatically divides material into many templates as needed, making it easy to meet quotas and import content. Note that templates automatically set the following properties. Maintain deletion policy. This allows policies to remain even if the stack is deleted. Change Replace to Delete. Revisions to the policy allow removal of the physical ID.

Recommended next steps

The figure shows two possible future steps.

After integrating rules into a CloudFormation stack, experts recommend retaining templates in a private Git repository. Manage imported policies with a continuous integration and delivery (CI/CD) pipeline and the Policies folder the tool automatically generates in the current local directory where the created templates were downloaded. Git repository version control allows pull requests, branch management, and history tracking. This process helps your team examine, update, and apply policies with better cooperation and control. Set up a CI/CD pipeline to automate CloudFormation stack updates to ensure regular and reliable updates. One suggestion is to install CloudFormation Hooks. CloudFormation Hooks can verify policies' syntax, security, and vulnerability reduction.

In conclusion

By integrating your AWS Organization's RCPs and SCPs into CloudFormation, you can manage and automate AWS governance efficiently. After import, you can maintain and change policies in CloudFormation to ensure company-wide consistency and version control. The program also creates a Policies folder in your current directory with downloaded templates for use as a central repository and with a continuous integration/continuous delivery pipeline. CloudFormation Hooks can test SCPs and RCPs against policy language and best practices to improve policy management. Centralising policy changes reduces misconfiguration and improves governance automation.

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DevOps on AWS: Part 9 | Maven Integration | Building a Foundation for Cl...

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AWS EC2 VM Setup | Run Springboot Microservice and Postgres DB in EC2 Se...

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Launching Visual update to AWS Management Console (preview)

Announcing the AWS Management Console’s visual update(preview)

AWS is revealing a preview of a visual update to the AWS Management Console today. The most recent version of Cloudscape, the Amazon Web Services (AWS) design system designed to create inclusive, relevant, and user-friendly AWS experiences at scale, is being utilized to roll out this upgrade.

In this article, we will discuss how the visual upgrade maintains the recognizable and consistent AWS Management Console experience while making it simpler for you to skim text, concentrate on the important details, and locate what you’re searching for more quickly.

Increased readability

You can find and comprehend your data more easily with a stronger visual hierarchy created by a reworked typographic scale and enhanced header treatment. You can distinguish important pieces of information more quickly if you use color and weight wisely across text elements. For instance, you’ll notice that form fields now have more noticeable labels, making scanning easier. This also holds for keys in key-value pairs and sections across components, including tabs, expandable elements, and service navigation.

AWS simplified the color treatment of interactive elements and enhanced the color pallet, making it more lively. For instance, many interface elements, including secondary buttons, links, tokens, and interactive statuses, have been changed to blue to facilitate interaction with the content on the screen and increase work efficiency.Image credit to AWS

Enhanced clarity in both light and dark modes

User concentration is supported by less complicated visuals. It harmonized the use of border styles across components and substituted a new, thinner stroke for drop shadows on key content wrappers, including cards, panels, and containers. This maximizes the available space inside the layout and minimizes visual noise. To simplify visual depth and enhance the overall content hierarchy, shadows are now only used to highlight particular interactive and transient elements.

To address the demand for more distinct element differentiation on the page, it has released modifications for dark mode. The color ramp has been updated, and the contrast between interactive states across components has been improved.

Updated user interface

In order to maintain consistent and recognizable experiences throughout AWS, it updated the UI while maintaining familiarity. Brighter colors, flatter shapes, and better layout treatment make the user experience easier on the eyes. The interface looks smoother and more natural with these improvements, which also improve its visual appeal.

It also included a whole new family of motion and visuals while maintaining the highest accessibility standards to provide a more enjoyable experience and encourage visual storytelling.

A higher density of information

More content is visible on the screen as a result of its optimization of information density through the reduction of unnecessary space. Now that related material is shown closer together, visual grouping is strengthened. You can take in more information at once since there is less space inside content wrappers like cards and containers. Wider and more focused, the new layout maximizes the experience for larger screen sizes than previously. The visual update improves and makes the AWS Management Console more user-friendly by making it easier to ingest information.

AWS also unveiled Toolbar, a fresh approach to navigating and gaining access to relevant tools and features. This maximizes the quantity of content on screen while assisting you in completing your activities.

Increased reliability

Now, the interface is more recognizable and reliable. In addition to enhancing a more dynamic and expressive experience, updated colors, iconography, and shapes support a consistent and seamless journey across all AWS experiences.

Now available

By going to the AWS Management Console, you can begin to see the visual update in a few consoles across all AWS Regions. The update will be applied to all services. You can now take advantage of a more readable and intuitive experience that enhances overall task efficiency because of the new graphic treatment.

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Now GA AWS Lambda SnapStart For Python and .NET Functions

AWS Lambda SnapStart

AWS Lambda SnapStart for Python and .NET functions is now generally available. It offers faster function startup performance, ranging from a few seconds to as little as a sub-second, usually requiring little to no code changes in Python, C#, F#, and Powershell.

AWS released Lambda SnapStart for Java functions on November 28, 2022, which can increase startup speed by up to ten times. Without allocating resources or investing time in intricate performance optimizations, Lambda SnapStart allows you to minimize outlier latencies caused by initializing functions.

Any one-time initialization code that is, code that executes only the first time a Lambda function is called can have its snapshotted memory and disk state cached and reused via Lambda SnapStart. Lambda creates a snapshot of the initialized execution environment’s memory and disk state using Firecracker microVM, encrypts it, and caches it for low-latency access.Image credit to AWS

Lambda improves startup latency by starting fresh execution environments from the cached snapshot rather than initializing them from scratch when you call the function version for the first time and as the invocations scale up. Using AWS Lambda, Lambda SnapStart makes it simple to create responsive and highly scalable Python and.NET applications.

Initialization code can cause a startup lag of several seconds for Python functions. This can happen, for example, while utilizing frameworks like Flask or Django or loading dependencies like LangChain, Numpy, Pandas, and DuckDB. Depending on the size of the model being utilized, loading ML models during initialization can take tens of seconds for many functions that also use Lambda for machine learning (ML) inference. For these situations, starting latency can be lowered from several seconds to as little as a sub-second by using Lambda SnapStart.

Because.NET just-in-time (JIT) compilation can take several seconds, it anticipates that most use cases will benefit from.NET functions. Customers have long been discouraged from using.NET for AWS Lambda due to the latency uncertainty that comes with initializing Lambda services. SnapStart caches a snapshot of a function’s memory and disk state so that it can resume fast. As a result, Lambda SnapStart will significantly reduce latency variability for the majority of.NET functions.

How to begin using Lambda SnapStart for.NET and Python

To begin, you can activate, update, and remove SnapStart for Python and.NET functions using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.

To use Lambda SnapStart, navigate to the Functions page in the AWS Lambda console and select your function. Choose General Configuration after selecting Configuration, and then select Edit. The Edit basic settings page displays SnapStart options.Image credit to AWS

Python 3.12 and later, as well as managed runtimes for.NET 8 and later, can be used to activate Lambda functions. After selecting Published versions, select Save.

Lambda initializes your code, takes a snapshot of the initialized execution environment, and caches the snapshot for low-latency access whenever you publish a new version of your function. To verify that SnapStart is activated, you can use the function.

To update the function configuration, use the AWS CLI command update-function-configuration with the –snap-start option.

Use the publish-version command to make a function version public.

Use the get-function-configuration command and enter the version number to verify that SnapStart is enabled for the function version.

A snapshot is available for the designated function version if the response indicates that OptimizationStatus is On and State is Active, indicating that SnapStart is engaged.

Runtime hooks

Code that was executed either before Lambda creates a snapshot or after Lambda resumes a function from a snapshot can be run using runtime hooks. Runtime hooks are helpful for cleaning up or releasing resources, updating configuration or other metadata dynamically, integrating with external systems or services (e.g., updating external state or sending notifications), and optimizing your function’s startup sequence (e.g., by preloading dependencies).

The open source Snapshot Restore for Python library, which is a component of the Python controlled runtime, offers Python runtime hooks. Two decorators,, which execute before Lambda creates a snapshot, and which execute when Lambda resumes a function from a snapshot, are provided by this library.

This Python handler example demonstrates how to execute code both before and after checkpointing:

from snapshot_restore_py import register_before_snapshot, register_after_restore

def lambda_handler(event, context):

# handler code

@register_before_snapshot

def before_checkpoint():

# Logic to be executed before taking snapshots

def after_restore():

# Logic to be executed after restore

.NET runtime hooks that are included in the Amazon.Lambda.Core package (version 2.5 or later) from NuGet can also be used. This library offers two methods: RegisterAfterRestore(), which runs after a function is resumed from a snapshot, and RegisterBeforeSnapshot(), which runs before to snapshot creation.

Things to be aware of

You should be aware of the following regarding Lambda SnapStart:

Managing uniqueness: If the content generated by your initialization code is unique and included in the snapshot, it will not be exceptional when it is reused in different execution settings. Suppose your code uses bespoke random number generation that doesn’t rely on built-in libraries or caches any information, such as DNS records, that can expire during startup. In that case, you must produce unique content after initialization to maintain uniqueness when using SnapStart.

Performance tuning: AWS advises you to initialize resources that cause startup latency and preload dependencies in your initialization code rather than in the function handler to optimize performance. By removing the latency brought on by extensive class loading from the invocation pipeline, SnapStart’s startup performance is improved.

Networking best practices:��When Lambda resumes your function from a snapshot, the connections that your function made during the initialization phase may not be in the same state. Network connections created by an AWS SDK are often automatically reestablished.

Function monitoring: You can use AWS X-Ray active tracing, Amazon CloudWatch log stream, the Telemetry API, Amazon API Gateway, and function URL metrics to get real-time telemetry data for extensions.

AWS Lambda SnapStart for Python and.NET functions is now accessible in the following AWS regions: Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), US East (N. Virginia), US East (Ohio), and US West (Oregon).

SnapStart pricing

For applications that are sensitive to latency, SnapStart can reduce starting time from many seconds to as little as a sub-second. In order to provide low-latency access, SnapStart takes a snapshot of the initialized memory (and disk) state of your function and caches it. Lambda reduces startup time by starting execution environments from this pre-initialized snapshot rather than starting them from scratch when your function is later called.

Every time you publish a new version of your function with SnapStart enabled, a snapshot is produced. For a minimum of three hours and then every millisecond after that, you will be charged for caching a snapshot while your function version is in use. The cost is determined by how much memory you give your function. Additionally, you pay a fee based on how much memory you devote to your function each time Lambda restores your snapshot to resume an execution environment.

SnapStart charges come in two flavors for the Python and.NET managed runtimes: the cost of caching a snapshot for each function version you publish with SnapStart enabled, and the cost of restoration for each instance of a function that is restored from a snapshot. Therefore, to lower your SnapStart cache expenses, remove any function versions that aren’t being used. For more pricing details Visit the AWS Lambda pricing page.

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Boosting Performance: CloudFront KeyValueStore Optimization

You may safely distribute both static and dynamic content with fast transfer speeds and minimal latency by using Amazon CloudFront. You can handle millions of requests per second and latency-sensitive customizations with CloudFront Functions. CloudFront Functions, for example, can be used to rewrite URLs, authorize requests, normalize cache keys, and change headers.

AWS are pleased to present CloudFront KeyValueStore, a safe, low-latency global key value datastore that can be accessed readly from within CloudFront Functions. This feature enables highly customized logic to be implemented at CloudFront edge locations.

In the past, configuration data had to be included directly into the function code. For instance, information for choosing which URL to send the viewer to when a URL needs to be redirected. Every tiny change in configuration when embedding it with the function code necessitates a code change and a redeployment of the function code. There is a chance that code will be accidentally altered when new lookup additions need updating and deploying code. Additionally, since the maximum function size is 10 KB, many use cases will struggle to fit all of the data within the code.

You can now update the function code and the data associated with it separately using CloudFront KeyValueStore. As a result, function code is made simpler and data updates are made simple without requiring code modifications to be deployed.

Let’s examine how this functions in real life.

Building a key value store for CloudFront

You select Functions from the navigation pane in the CloudFront dashboard. Then now you select Create KeyValueStore under the KeyValueStores menu.

This allows you to import key-value pairs into an Amazon Simple Storage Service (Amazon S3) bucket from a JSON file. If you want to start with no keys, therefore you not doing that right now. You finish the key value store creation by entering a name and description.

You select Edit under the Key value pairs area and then Add pair once the key value store has been established. You enter Hello World for the value and hello for the key before saving the adjustments. For now, one key is sufficient, but you can add more keys and values.

Changes made to a key value store propagate quickly to all CloudFront edge locations, allowing functions connected with the key value store to use it with little latency.

Utilizing CloudFront Functions’ CloudFront KeyValueStore

You select Functions from the navigation pane in the CloudFront console, followed by Create function. You give the function a name, choose the cloudfront-js-2.0 runtime, and finish the function’s creation. Then you correlate this function with the key value store using the newly available option.

You can use the console’s key value store ID, which you copy, in the function code that follows:

This function answers with the name of the key and its value, using the first segment of the request path as the key.

Now you publish the function and save the modifications. You can link the function to a CloudFront distribution which you previously made in the Publish tab of the function. You can intercept all requests to the distribution using the Default (*) cache action and the Viewer Request event type.

You return to the functions list in the console and watch for the function to be deployed. Next, you download content from the distribution using curl from the command line and verify the function’s outcome.

Initially, you can test a few pathways that call the method and checkup the previous key you made (hello): Success! Next, you experiment with a different approach to observe that, in the event that the key cannot be retrieved, the code returns the default value.

Now that we have this basic example working, let’s try a more sophisticated and practical one.

Using CloudFront KeyValueStore configuration data, rewrite the URL

Let’s create a function that looks up the custom path that CloudFront should use to send the actual request in a key-value store using the content of the URL in the HTTP request. This feature can assist in managing the various services that make up a website.

Things to consider

Today, CloudFront KeyValueStore is accessible in every edge location across the world. Pay just for the read/write operations from the public API and the read operations from within CloudFront Functions when using CloudFront KeyValueStore. View the CloudFront pricing page for additional details.

The AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs can all be used to manage a key value store. Support for AWS CloudFormation is on the horizon. You can link a single key value store to every function, and key value stores have a maximum capacity of 5 MB. A key can have a maximum size of 512 bytes. Values may have a maximum value of 1KB. Using a source file on Amazon S3, you can import key/value data while building a key-value store. This file has the following JSON structure:

Key/value data imports during creation provide easy configuration replication between environments (e.g., preproduction and production) and can automate the setup of a new environment (e.g., test or dev).

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Create AWS EC2 Instance | Install Postgres DB in AWS Cloud EC2 Server

Hello friends, a new #video on #aws #cloud #ec2 #server setup and #postgres setup in #ec2instance is published on #codeonedigest #youtube channel. Learn #awsec2 #postgressetup  #programming #coding with codeonedigest.

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