Forem: GargeeBhatnagar

Deep Dive into Amazon Security Lake for a Specific Account Across All Regions

GargeeBhatnagar — Sat, 11 Apr 2026 14:30:48 +0000

“ I have checked the documents of AWS for deep dive into amazon security lake for a specific account across all regions. Amazon security lake makes it easy for logs and events data collection. In terms of cost, the solution is cheaper and secure.”

Amazon Security Lake is a fully managed security data lake service. You can use security lake to automatically centralize security data from AWS environments, SaaS providers, on premises, cloud sources and third-party sources into a purpose built data lake that’s stored in your AWS account. It is backed by Amazon Simple Storage Service buckets, meaning you retain ownership of the data you collect. Security Lake simplifies collecting security data and gathering insights from it. This supports a wide range of use cases with the ultimate goal of improving the protection of your workloads, applications and data.

In this post, you will experience the deep dive into amazon security lake for a specific account across all regions. Here I have an aws organization with a delegated administrator for security lake. And in the member account, I have enabled and set up the security lake for management account logs trace and also the billing of member account is managed by management account itself.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, security lake, s3 bucket, athena, cloudwatch logs, aws organization.

Solution Overview

The blog post consists of the following phases:

Member Account Add-on in Management Account under AWS Organizations for Enable of Security Lake Service
Enable and SetUp of Amazon Security Lake for Specific AWS Account for All Supported Regions in Member Account
Run of Queries in Amazon Athena and Output of S3 Bucket with CloudWatch Logs

Phase 1: Member Account Add-on in Management Account under AWS Organizations for Enable of Security Lake Service

Open the AWS Organizations console in the management account, click on add an aws account option and send the invitation to the member account. Goto member account and accept the invitation in aws organization service. Once the invitation is accepted, you can see the member account in aws organization of management account. Now in management account, enable the security lake under security lake aws service and provide member account number under delegate administrator.

Phase 2: Enable and SetUp of Amazon Security Lake for Specific AWS Account for All Supported Regions in Member Account

Phase 3: Run of Queries in Amazon Athena and Output of S3 Bucket with CloudWatch Logs

Clean-up

S3 bucket, cloudwatch logs, aws organization, security lake.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon Security Lake = $0.00

Cost of Simple Storage Service = $0.01

Cost of CloudWatch Logs = $0.00

Total Cost = $0.01

Summary

In this post, I showed “deep dive into amazon security lake for a specific account across all regions”.

For more details on Amazon Security Lake, Checkout Get started Amazon Security Lake, open the Amazon Security Lake console. To learn more, read the Amazon Security Lake documentation.

Thanks for reading!

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Deep Dive into Default and Custom AWS Security Groups

GargeeBhatnagar — Mon, 30 Mar 2026 18:15:31 +0000

“ I have checked the documents of AWS for deep dive into default and custom aws security groups. AWS security groups make it easy for traffic control between AWS resources and others. In terms of cost, the solution is cheaper and secure.”

AWS Security Groups act as a virtual firewall for cloud resources (like AWS EC2 instances) or network traffic, controlling inbound and outbound traffic through defined rules. It operates at the instance level rather than the subnet level, allowing only permitted traffic, and is stateful, meaning return traffic is automatically allowed.

In this post, you will experience the deep dive into default and custom aws security groups. Here I have a default security group created, a custom security group which I can create manually or through automation and a new security group creation from an existing security group.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, security group, ec2, vpc and aws services.

Solution Overview

The blog post consists of the following phases:

Updation of Default Security Group for Inbound Rule and Outbound Rule
Creation of New Security Group in Security Groups Console
Creation of New Security Group Using Existing Security Group
Deletion of Manually Created Security Group and Not the Default Security Group

Phase 1: Updation of Default Security Group for Inbound Rule and Outbound Rule

Open the Security Groups console, verify the security group created with the default name. Check the default security group rule tabs for inbound rule, outbound rule, sharing and vpc associations. You can edit the inbound rule and outbound rule as per requirement.

Phase 2: Creation of New Security Group in Security Groups Console

Phase 3: Creation of New Security Group Using Existing Security Group

Phase 4: Deletion of Manually Created Security Group and Not the Default Security Group

Clean-up

Security Groups.

Pricing

I review the pricing and estimated cost of this example.

Cost of Security Groups = $0.00

Total Cost = $0.00

Summary

In this post, I showed “deep dive into default and custom aws security groups”.

For more details on Security Groups, Checkout Get started Security Groups, open the Security Groups console. To learn more, read the Security Groups documentation.

Thanks for reading!

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Deep Dive on Agent Space and DevOps Agent Web App in AWS DevOps Agent

GargeeBhatnagar — Sat, 14 Mar 2026 11:46:11 +0000

“ I have checked the documents of AWS for deep dive on agent space and devops agent web app in aws devops agent. AWS devops agent makes it easy for space creation and cloudwatch alarms for tracking cpu utilization. In terms of cost, the solution is cheaper and secure.”

AWS DevOps Agent is a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance. AWS DevOps Agent investigates incidents and identifies operational improvements as an experienced devops engineer.

In this post, you will experience the deep dive on agent space and devops agent web app in aws devops agent. Here I have created an agent space for devops agent web app and created the resources as ec2 instance, cloudwatch alarm, security group and key pair to track the ec2 instance cpu utilization in cloudwatch alarm.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, aws devops agent, cloudwatch alarm, cloudformation stack, ec2 instance.

Solution overview

The blog post consists of the following phases:

Creation of Agent Space in AWS DevOps Agent
Creation of Resources as EC2 Instance, CPU Alarm, Security Group and Key Pair Using CloudFormation Stack
Output as CloudWatch Alarm CPU Utilization

Phase 1: Creation of Agent Space in AWS DevOps Agent

Open the AWS DevOps Agent console, goto agent spaces option. Create agent space with required name and leave other options as default with select of auto-create iam role for agent space and web app. Once the agent space is created, we can do web app link access via operator access option.

Phase 2: Creation of Resources as EC2 Instance, CPU Alarm, Security Group and Key Pair Using CloudFormation Stack

Phase 3: Output as CloudWatch Alarm CPU Utilization

Clean-up

Cloudformation stack and agent space.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon CloudWatch =
$0.00 per alarm metric month - first 10 alarm metrics x 0 alarms = $0.00

Cost of AWS CloudFormation =
$0.0 per Operations for Resource-Invocation-Count:ProcessResourceHandlers usage x 8 count = $0.00

Cost of Amazon Elastic Compute Cloud =
$0.0104 per On Demand Linux t3.micro Instance Hour x 0.326 Hrs = $0.00

Cost of AWS DevOps Agent = $0.00

Total Cost = $0.00

Summary

In this post, I showed “deep dive on agent space and devops agent web app in aws devops agent”.

For more details on AWS DevOps Agent, Checkout Get started AWS DevOps Agent, open the AWS DevOps Agent console. To learn more, read the AWS DevOps Agent documentation.

Thanks for reading!

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Deep Dive on Loop Filter as Metric Filter in Amazon CloudWatch

GargeeBhatnagar — Fri, 20 Feb 2026 13:09:02 +0000

“ I have checked the documents of AWS for deep dive on loop filter as metric filter in amazon cloudwatch. Amazon cloudwatch makes it easy for metric filter and alarms. In terms of cost, the solution is cheaper and secure.”

Metrics are the fundamental concept in cloudwatch. A metric represents a time-ordered set of data points that are published to cloudwatch. Think of a metric as a variable to monitor and the data points as representing the values of that variable over time. For example, the cpu usage of a particular ec2 instance is one metric provided by amazon ec2. The data points themselves can come from any application or business activity from which you collect data.

By default, many AWS services provide metrics at no charge for resources. For a charge, you can also enable detailed monitoring for some resources, such as your amazon ec2 instance or publish your own application metrics. For custom metrics, you can add the data points in any order and at any rate you choose. You can retrieve statistics about those data points as an ordered set of time-series data.

In this post, you will experience the deep dive on loop filter as metric filter in amazon cloudwatch. Here I have uploaded the file in s3 bucket which api call logs stored in amazon cloudwatch via trace by cloudtrail. In the cloudwatch log group, added a metric filter and alarm with sns notification.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, amazon cloudwatch, sns, cloudtrail, s3 bucket.

Solution overview

The blog post consists of the following phases:

Enable of Server Access Logging in S3 Bucket and Track Log Stream in CloudWatch Log Group
Create of Loop Filter as Metric Filter on CloudWatch Log Group and Set of Alarm on Metric Filter

Phase 1: Enable of Server Access Logging in S3 Bucket and Track Log Stream in CloudWatch Log Group

Open the Amazon S3 console, goto properties tab and check for server access logging option. Enable the server access logging option with a set of server logs destination s3 bucket. Upload a file in testdeta bucket to get its api call log in cloudtrail as log stream of cloudwatch log group. Also we can set a sns topic and subscription for notification purpose.

Phase 2: Create of Loop Filter as Metric Filter on CloudWatch Log Group and Set of Alarm on Metric Filter

Clean-up

Amazon cloudwatch: log group, log stream and alarm, s3 bucket, cloudtrail, sns.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon CloudWatch = $0.00
Amazon CloudWatch :
$0.00 per alarm metric month - first 10 alarm metrics for 0 Alarms = $0.00
$0.00 per metric-month - first 10 metrics for 0.001 metrics = $0.00
AmazonCloudWatch PutLogEvents :
First 5GB per month of log data ingested is free for 0.001GB = $0.00

Cost of CloudTrail = $0.00
AWS CloudTrail USE1-FreeEventsRecorded : 0.0 per free event recorded for 827 events = $0.00

Cost of Simple Storage Service = $0.01

Cost of Simple Notification Service = $0.00

Total Cost = $0.01

Summary

In this post, I showed “deep dive on loop filter as metric filter in amazon cloudwatch”.

For more details on Amazon CloudWatch, Checkout Get started Amazon CloudWatch, open the Amazon CloudWatch console. To learn more, read the Amazon CloudWatch documentation.

Thanks for reading!

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Creation of Workspace in Web Application Using AWS Transform

GargeeBhatnagar — Sun, 08 Feb 2026 11:56:25 +0000

“ I have checked the documents of AWS for the creation of workspace in web application using aws transform. AWS transform makes it easy and secure for web applications. In terms of cost, the solution is cheaper and secure.”

AWS Transform is a service that helps you accelerate and simplify the transformation of infrastructure, applications and code with an agentic AI experience. It provides specialized tools to streamline modernization and migration efforts across various workloads.

In this post, you will experience the creation of workspace in web application using aws transform. Here I have enabled the web application with addition of users to make sure user can able to access the transform workspace.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, aws transform, iam identity center, users, web application url.

Solution overview

The blog post consists of the following phases:

Enable of Web Application in AWS Transform and Add Users to Access AWS Transform
Output of Web Application to Create AWS Transform Workspace

Phase 1: Enable of Web Application in AWS Transform and Add Users to Access AWS Transform

Open the AWS Transform console, click on get started. On the account settings, choose default aws managed key. Choose enable web application then give access as iam identity center. You can further check settings and add users to access the transform.

Phase 2: Output of Web Application to Create AWS Transform Workspace

Clean-up

Aws transform: service profile and users, iam identity center.

Pricing

I review the pricing and estimated cost of this example.

Cost of AWS Transform = $0.00

Cost of IAM Identity Center = $0.00

Total Cost = $0.00

Summary

In this post, I showed “creation of workspace in web application using aws transform”.

For more details on AWS Transform, Checkout Get started AWS Transform, open the AWS Transform console. To learn more, read the AWS Transform documentation.

Thanks for reading!

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Creation of Jupyter Notebook Instance Using Amazon Braket

GargeeBhatnagar — Wed, 28 Jan 2026 11:19:35 +0000

“ I have checked the documents of AWS for the creation of jupyter notebook instance using amazon braket. Amazon braket makes it easy and secure for notebook instance purposes. In terms of cost, the solution is cheaper and secure.”

Amazon Braket is a fully managed quantum computing service provided by AWS. It enables researchers, scientists and developers to explore quantum computing by offering tools to design, simulate and execute quantum algorithms on various quantum hardware and simulators. It simplifies access to quantum technologies without requiring users to manage cloud infrastructure.

In this post, you will experience the creation of jupyter notebook instance using amazon braket. Here I have created the notebook instance with iam role and permissions in amazon braket.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, amazon braket, iam role, cloudwatch logs and notebook instance url.

Solution overview

The blog post consists of the following phases:

Create of Notebook Instance via Amazon Braket
Output of Notebook Instance Logs, Notebook Instance and Iam Role

Phase 1: Create of Notebook Instance via Amazon Braket

Open the Amazon Braket console, click on get started. On the service dashboard, create a notebook via create a service-linked role then click on accept and enable option. Skip the create spending limit and then create the execution role with name the notebook instance and open the notebook. Once the notebook instance is ready, then open the sagemaker notebook instance. Also you can further check the execution role and also stop the notebook instance if not in use further or delete it.

Phase 2: Output of Notebook Instance Logs, Notebook Instance and Iam Role

Clean-up

Amazon braket : notebook instance, iam role and cloudwatch logs.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon Sagemaker =

Amazon Sagemaker createvolume-gp2 = $0.14 per GB-Mo of Notebook Instance ML storage for 0.004 GB-Mo = $0.00

Amazon Sagemaker runinstance = $0.05 per Notebook ml.t3.medium hour in US East (N. Virginia) for 0.53 Hrs = $0.03

Cost of Amazon CloudWatch = AmazonCloudWatch PutLogEvents for first 5GB per month of log data ingested is free = $0.00

Cost of Amazon Braket = $0.00

Total Cost = $0.03

Summary

In this post, I showed “creation of jupyter notebook instance using amazon braket”.

For more details on Amazon Braket, Checkout Get started Amazon Braket, open the Amazon Braket console. To learn more, read the Amazon Braket documentation.

Thanks for reading!

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Custom Policy Creation and Authorization Using Amazon Verified Permissions

GargeeBhatnagar — Fri, 23 Jan 2026 18:49:24 +0000

“ I have checked the documents of AWS for custom policy creation and authorization using amazon verified permissions. Amazon verified permissions make it easy and secure for policy creation and authentication purposes. In terms of cost, the solution is cheaper and secure.”

Amazon verified permissions is a fully managed authorization service that uses the cedar policy language, so you can build more secure applications. With verified permissions, developers can build applications faster by externalizing authorization and centralizing policy management. They can also align authorization within the application with zero trust principles. Security and audit teams can better analyze and audit who has access to what within applications.

In this post, you will experience custom policy creation and authorization using amazon verified permissions. Here I have created the custom static policy with resource action under policy scope and also run the authorization to check if the policy is satisfied or still having issue.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, amazon verified permissions, authorization request and policy.

Solution overview

The blog post consists of the following phases:

Create of Sample Policy via Sample Policy Store Option
Create of Static Policy for Custom Policy Scope
Output of Test Bench via Run of Authorization Request for Policy Satisfaction

Phase 1: Create of Sample Policy via Sample Policy Store Option

Open the Amazon Verified Permissions console, click on create policy store. Choose to start from a sample policy store option, select photoflash under sample project. Leave other options default and create the policy store. You can next goto overview to view the policy store created.

Phase 2: Create of Static Policy for Custom Policy Scope

Under the Policies tab, choose create static policy option. In policy scope, choose effect as permit then principals scope as specific principal then resources scope as specific resource then resource this policy should apply to as photoflash:album and name of it. Under actions scope, select specific set of actions and required policy apply on it. Specify the description and create a policy.

Phase 3: Output of Test Bench via Run of Authorization Request for Policy Satisfaction

Clean-up

Amazon verified permissions : policies, policy templates.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon Verified Permissions =

Amazon Verified Permissions CreatePolicy = 40 USD per million CreatePolicy API requests for 2 API Requests = $0.00

Amazon Verified Permissions GetPolicy = 40 USD per million GetPolicy API requests for 4 API Requests = $0.00

Amazon Verified Permissions ListPolicies = 40 USD per million ListPolicies API requests for 7 API Requests = $0.00

Amazon Verified Permissions SingleAuthorizationRequest = 5 USD per million Single Authorization Requests for 1 API Requests = $0.00

Total Cost = $0.00

Summary

In this post, I showed “custom policy creation and authorization using amazon verified permissions”.

For more details on Amazon Verified Permissions, Checkout Get started Amazon Verified Permissions, open the Amazon Verified Permissions console. To learn more, read the Amazon Verified Permissions documentation.

Thanks for reading!

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Import YAML Runbook as an AWS Systems Manager Automation Document to Delete Unattached EBS Volumes

GargeeBhatnagar — Thu, 18 Dec 2025 11:58:49 +0000

“ I have checked the documents of AWS to import yaml runbook as an aws systems manager automation document to delete unattached ebs volumes. AWS systems manager makes it easy and secure for runbook import as yaml and run it for deletion activity with version control and follow flow steps in runbook. In terms of cost, the solution is cheaper and secure.”

AWS Systems Manager helps you centrally view, manage and operate nodes at scale in AWS, on-premises and multicloud environments. With the launch of a unified console experience, systems manager consolidates various tools to help you complete common node tasks across AWS accounts and AWS regions.

In this post, you will experience import yaml runbook as an aws systems manager automation document to delete unattached ebs volumes. Here I have imported the yam file in runbook for ssm document creation and then executed the automation runbook to identify, list, approve and delete the unattached ebs volumes.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, aws systems manager, iam role, iam user, ebs volume, amazon ec2, documents.

Solution overview

The blog post consists of the following phases:

Create of SSM Document as Runbook in AWS Systems Manager
Output of Executing Automation Runbook for Deletion of Unattached EBS Volumes

Phase 1: Create of SSM Document as Runbook in AWS Systems Manager

Open the AWS Systems Manager console, goto documents option under change management tools and create an automation document as create of runbook from yaml file with import runbook option. We can see the flow diagram once the import is completed as the steps of flow mentioned. The parameters are present where we need to give input value during the runbook execution initiation. Once the document creation completes, we can see the document under owned by me option in the document tab. Whenever we need to have an updated yaml file in runbook, we can create a new version as a default one with a new changed yaml file. Make sure to make the version as default else it will not execute the current changed yaml file.

Phase 2: Output of Executing Automation Runbook for Deletion of Unattached EBS Volumes

Clean-up

AWS systems manager : documents, iam role, ec2 instance, ebs volume and iam user.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon Elastic Compute Cloud = $0.05

Cost of Systems Manager = $0.00
[ AWS Systems Manager AWS-Auto-ScriptDuration-Tier3 for 22000 units = $0.00
AWS Systems Manager AWS-Auto-Steps-Tier1 for 52 steps = $0.00 ]

Total Cost = $0.05

Summary

In this post, I showed “import yaml runbook as an aws systems manager automation document to delete unattached ebs volumes”.

For more details on AWS Systems Manager, Checkout Get started AWS Systems Manager, open the AWS Systems Manager console. To learn more, read the AWS Systems Manager documentation.

Thanks for reading!

Connect with me: Linkedin

Deploy the Application and Version Control Using Amazon Elastic Beanstalk

GargeeBhatnagar — Fri, 12 Dec 2025 14:17:50 +0000

“ I have checked the documents of AWS to deploy the application and version control using amazon elastic beanstalk. Amazon elastic beanstalk makes it easy and secure for version control and deployment of application. In terms of cost, the solution is cheaper and secure as there is no additional cost for beanstalk service, only we need to pay for ec2 instance, s3 bucket and other service in-use.”

With Elastic Beanstalk you can deploy web applications into the AWS cloud on a variety of supported platforms. You build and deploy your applications. Elastic beanstalk provisions amazon ec2 instances, configures load balancing, sets up health monitoring and dynamically scales your environment.

In this post, you will experience deploy the application and version control using amazon elastic beanstalk. Here I have created the application for beanstalk and done version control of reploy application option.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, amazon elastic beanstalk, s3 bucket, iam role, amazon ec2, cloudformation, browser.

Solution overview

The blog post consists of the following phases:

Create of Application in Amazon Elastic Beanstalk
Output of Domain as Application Deployed

Phase 1: Create of Application in Amazon Elastic Beanstalk

Open the Amazon Elastic Beanstalk console, click on create application option. Choose the environment tier, specify the application name and else keep other option default one with sample application and single instance. Select the service access option with iam role created as iam service role for beanstalk and iam role for ec2. Choose default only for networking, database, tag, instance traffic, scaling, updates, monitoring, logging. Review the details and click on create option. Once the beanstalk environment is ready, we can identify it as health is OK. Also we can redeploy the application with change and upload the change with creation of the version of it.

Phase 2: Output of Domain as Application Deployed

Clean-up

Amazon beanstalk: application and environment, iam role, ec2 instance, s3 bucket, security group, cloudformation stack, auto scaling group.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon Elastic Compute Cloud = $0.00

Cost of Simple Storage Service = $0.01

Cost of CloudFormation = $0.00

Total Cost = $0.01

Summary

In this post, I showed “deploy the application and version control using amazon elastic beanstalk”.

For more details on Amazon Elastic Beanstalk, Checkout Get started Amazon Elastic Beanstalk, open the Amazon Elastic Beanstalk console. To learn more, read the Amazon Elastic Beanstalk documentation.

Thanks for reading!

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Explore the Amazon Augmented AI with Human Review Workflows, Amazon Textract Form Extraction Task and Amazon Cognito User Pool

GargeeBhatnagar — Wed, 19 Nov 2025 09:30:44 +0000

“ I have checked the documents of AWS to explore the amazon augmented ai with human review workflows, amazon textract form extraction task and amazon cognito user pool.. Amazon augmented ai makes it easy and secure to have human review workflows for amazon textract. In terms of cost, the solution is cheaper and secure.”

Amazon Augmented AI(Amazon A2I) is designed to allow you to integrate your workflow with Amazon Textract for document processing and Amazon Rekognition for content moderation, so you can implement human review workflows in just a few clicks. The Amazon A2I API is also designed to allow you to integrate your workflows into custom models that you have built with Amazon Sagemaker or other machine learning tools.

Amazon A2I supports multiple choices for human reviewers. You can use your private team of reviewers for in-house review jobs, especially when handling sensitive data that needs to stay within your organization.

In this post, you will experience explore the amazon augmented ai with human review workflows, amazon textract form extraction task and amazon cognito user pool. Here I have explored the human reviews workflows feature and amazon textract extraction task with user authentication with cognito user pool.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, amazon augmented ai, s3 bucket, amazon cognito and amazon textract.

Solution overview

The blog post consists of the following phases:

Create of Private Workforce in Amazon SageMaker AI
Create of Human Review Workflow in Amazon Augmented AI with Amazon Textract
Output of Labeling Project for Human Review

Phase 1: Create of Private Workforce in Amazon SageMaker AI

Open the Amazon SageMaker AI console, choose the labeling workforces parameter under ground truth. Create the private team with select of private team creation "create a private team with aws cognito”, specify the team name, choose worker as “invite new workers by email” with specify of email addresses, organization name and contact email. Optionally we can set sns notification also in amazon sagemaker ai. Once complete, we can create the private team as a private workforce. Also we have an s3 bucket with text file screenshot uploaded in the bucket.

Phase 2: Create of Human Review Workflow in Amazon Augmented AI with Amazon Textract

Under the Amazon Augmented AI parameter, choose the human review workflows parameter option and create it with specifying the s3 bucket created, workflow settings as specifying name, s3 bucket, iam role, choose task type “textract” and set value for amazon textract from extraction, specify the worker task template name and task description. Also select private teams created and create the human review workflow.

Phase 3: Output of Labeling Project for Human Review

Clean-up

Amazon sagemaker ai : private team, workers, private workforce and human review workflows, s3 bucket and amazon cognito : domain and user pool.

Pricing

I review the pricing and estimated cost of this example.

Cost of Amazon SageMaker AI = $0.00

Cost of Amazon Cognito = $0.00

Cost of Simple Storage Service = $0.01

Total Cost = $0.01

Summary

In this post, I showed “explore the amazon augmented ai with human review workflows, amazon textract form extraction task and amazon cognito user pool”.

For more details on Amazon Augmented AI, Checkout Get started Amazon Augmented AI, open the Amazon Augmented AI console. To learn more, read the Amazon Augmented AI documentation.

Thanks for reading!

Connect with me: Linkedin

Deep Dive into Farm, Fleet and Queue Features of AWS Deadline Cloud

GargeeBhatnagar — Mon, 10 Nov 2025 18:51:17 +0000

“ I have checked the documents of AWS to deep dive into farm, fleet and queue features of aws deadline cloud. AWS Deadline Cloud makes it easy and secure to create and manage rendering projects and jobs on ec2 instances directly. In terms of cost, the solution is cheaper and secure.”

AWS Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated 2D/3D graphics and visual effects for films, TV shows, commercials, games, and industrial design. With Deadline Cloud, you can set up, deploy, and scale rendering projects in minutes, so you can improve the efficiency of your rendering pipelines and take on more projects.

In this post, you will experience the deep dive into farm, fleet and queue features of aws deadline cloud. Here I have explored the feature of deadline cloud which creates and manages the rendering projects and jobs on ec2 instances directly.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, aws deadline cloud, user, group, s3 bucket and aws deadline cloud portal.

Solution overview

The blog post consists of the following phases:

Explore of AWS Deadline Cloud with Farm, Fleet and Queue
Output as AWS Deadline Cloud

Phase 1: Explore of AWS Deadline Cloud with Farm, Fleet and Queue

Open the AWS Deadline Cloud console, set up the deadline cloud with creation of farms, fleets and queues. Write the monitor name and verify the aws region with aws service role. Create a farm with group and user details add-on. Write the queue name with s3 bucket folder location and other options as default. Create a fleet with fleet name, choose an instance type as on-demand instance and set service role for fleet. Review the details of setup and then click on create farm option. Once creation successful and onboarding complete, check the dashboard of aws deadline cloud.

Phase 2: Output as AWS Deadline Cloud

Clean-up

AWS deadline cloud, roles, s3 bucket and log groups.

Pricing

I review the pricing and estimated cost of this example.

Cost of AWS Deadline Cloud = $0.00

Total Cost = $0.00

Summary

In this post, I showed “deep dive into farm, fleet and queue features of aws deadline cloud”.

For more details on AWS Deadline Cloud, Checkout Get started AWS Deadline Cloud, open the AWS Deadline Cloud console. To learn more, read the AWS Deadline Cloud documentation.

Thanks for reading!

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Explore Canvas and Templates in AWS Infrastructure Composer

GargeeBhatnagar — Sat, 18 Oct 2025 17:37:14 +0000

“ I have checked the documents of AWS to explore canvas and templates in aws infrastructure composer. AWS Infrastructure Composer makes it easy and secure to create canvas and template yaml(or json) file to get ready for building applications . In terms of cost, the solution is cheaper and secure.”

AWS Infrastructure Composer allows you to visually compose modern applications on AWS. More specifically, you can use infrastructure composer to visualize, build and deploy modern applications from all AWS services that are supported by AWS CloudFormation without needing to be an expert in AWS CloudFormation.

As you compose your AWS CloudFormation through a delightful drag and drop interface, infrastructure composer creates your infrastructure as code templates, all while following AWS best practices.

In this post, you will experience the explore canvas and templates in aws infrastructure composer. Here I have explored the demo for creating canvas as an architecture diagram and template yaml to build the application as a ready file.

Architecture Overview

The architecture diagram shows the overall deployment architecture with data flow, aws infrastructure composer, canvas, template and app.

Solution overview

The blog post consists of the following phases:

Explore of AWS Infrastructure Composer with Canvas and Template
Output as Canvas and Template File

Phase 1: Explore of AWS Infrastructure Composer with Canvas and Template

Open the AWS infrastructure composer console, explore the demo to check the options and features available in infrastructure composer so that you are able to build the application with architect design canvas and template. The architect diagram is called canvas, template in yaml(json), list defines item details, resources defines aws service list. As I want to add a lambda function, we can drag it from the resource section and keep it in canvas then to connect lambda with dynamodb use the right side of dynamodb to the right side of lambda in cards. We can save the template or export the canvas under the menu section.

Phase 2: Output as Canvas and Template File

Clean-up

Infrastructure composer project.

Pricing

I review the pricing and estimated cost of this example.

Cost of AWS Infrastructure Composer = $0.00

Total Cost = $0.00

Summary

In this post, I showed “explore canvas and templates in aws infrastructure composer”.

For more details on AWS Infrastructure Composer, Checkout Get started AWS Infrastructure Composer, open the AWS Infrastructure Composer console. To learn more, read the AWS Infrastructure Composer documentation.

Thanks for reading!

Connect with me: Linkedin