Running applications in a Kubernetes cluster has many advantages, including scalability, flexibility, and ease of management. However, to make our applications highly available and resilient, we often need a load balancer to distribute the incoming traffic across multiple pods or nodes. Amazon Web Services (AWS) offers the Elastic Load Balancer (ELB) service, which can be integrated with our Kubernetes cluster to achieve this. This blog post will explore how to manage ELB for a Kubernetes cluster using the AWS Load Balancer Controller.

What is the AWS Load Balancer Controller?

The AWS Load Balancer Controller is an open-source project that simplifies the integration of AWS Elastic Load Balancers with Kubernetes clusters. A Kubernetes Ingress Controller automates the creation and management of AWS load balancers. This controller enables us to define Kubernetes resources like Ingress, Services, and Network Load Balancers as custom resources, making it easy to configure and manage AWS load balancers directly from our Kubernetes cluster.

Prerequisites:

Before we start managing ELBs with the AWS Load Balancer Controller, we should have the following prerequisites in place:

• An AWS account with appropriate permissions to create and manage load balancers.
• A running AWS EKS cluster.
• AWS CLI installed.
• Kubectl, the Kubernetes command-line tool, installed and configured to access our cluster.
• Helm, the package manager for Kubernetes, installed. In this example we will be using Helm for hassle-free installation.

Configuring the AWS Load Balancer Controller: 

After installing the controller, we must configure it to manage our AWS load balancers. We can do this by creating an IAM policy, role, and ServiceAccount for the controller, as well as defining the necessary AWS annotations in our Kubernetes resources.

1. Create an IAM policy granting the controller the necessary permissions to manage AWS       load balancers. We can use the AWS CLI to create this policy.

1. Run the following command to download the policy document from github.

# curl -o iam-policy.json https://raw.githubusercontent.com/kubernetes-sigs/aws-load-balancer-controller/main/docs/install/iam_policy.json

• To create an IAM policy named AWSLoadBalancerControllerIAMPolicy for our worker node instance profile, run the following command:

# aws iam create-policy –policy-name AWSLoadBalancerControllerIAMPolicy I am running a few minutes late; my previous meeting is running over.

    –policy-document file://iam-policy.json

2. Create an IAM role and associate the IAM policy with it. Make sure to trust the AWS service account for the controller.

1. To get the cluster’s OIDC provider URL, run the below command

  # aws eks describe-cluster –name <CLUSTER_NAME> –query “cluster.identity.oidc.issuer” I am running a few minutes late; my previous meeting is running over.

–output text

• The output will be something like this.

‘oidc.eks.<REGION_CODE>.amazonaws.com/id/EXAMPLE1234OI5DC1234OI5DCEXAMPLE”

• Next, copy the following contents and Replace <ACCOUNT_ID> with your AWS account ID. Replace <REGION_CODE> with the AWS Region in which the cluster is in. Replace <OIDC_URL> with the output returned in the previous step. After replacing the text, run the modified command to create the load-balancer-role-trust-policy.json file.

# cat >load-balancer-role-trust-policy.json <<EOF

  {

      “Version”: “2012-10-17”,

      “Statement”: [

          {

              “Effect”: “Allow”,

              “Principal”: {

                  “Federated”: “arn:aws:iam::<ACCOUNT_ID>:oidc-provider/<OIDC_URL>”

              },

              “Action”: “sts:AssumeRoleWithWebIdentity”,

              “Condition”: {

                  “StringEquals”: {

                      “<OIDC_URL>: “sts.amazonaws.com”,

                      “<OIDC_URL>:sub”: “system:serviceaccount:kube-system:aws-load-balancer-controller”

                  }

              }

          }

      ]

  }

  EOF

• Create the IAM role.

    # aws iam create-role I am running a few minutes late; my previous meeting is running over.

      –role-name AmazonEKSLoadBalancerControllerRole I am running a few minutes late; my previous meeting is running over.

      –assume-role-policy-document file://”load-balancer-role-trust-policy.json“

  5. Attach the required Amazon EKS-managed IAM policy to the IAM role. Replace <ACCOUNT_ID> with our AWS account ID.

  # aws iam attach-role-policy \

    –policy-arn arn:aws:iam::<ACCOUNT_ID>:policy/AWSLoadBalancerControllerIAMPolicy \

    –role-name AmazonEKSLoadBalancerControllerRole

3. Installing the AWS load balancer controller add-on

 1. Run the update-kubeconfig AWS command to update the cluster name in the kubeconfig file and confirm that it updates the config file under ~/.kube/config:

      # aws eks –region <REGION_CODE> update-kubeconfig –name cluster_name

2. Create the Kubernetes service account on our cluster. The Kubernetes service account named aws-load-balancer-controller is annotated with the IAM role that was created in the name AmazonEKSLoadBalancerControllerRole.

      # cat >aws-load-balancer-controller-service-account.yaml <<EOF

      apiVersion: v1

      kind: ServiceAccount

      metadata:

        labels:

          app.kubernetes.io/component: controller

          app.kubernetes.io/name: aws-load-balancer-controller

        name: aws-load-balancer-controller

        namespace: aws-load-balancer-controller

        annotations:

          eks.amazonaws.com/role-arn: arn:aws:iam::<ACCOUNT_ID>:role/AmazonEKSLoadBalancerControllerRole

      EOF

 3. Run the below kubectl command to create the service account

      # kubectl apply -f aws-load-balancer-controller-service-account.yaml

4. Install the AWS Load Balancer Controller using Helm V3

    To install the AWS Load Balancer Controller, follow these steps:

1. First, add the Helm chart repository for the AWS Load Balancer Controller:

# helm repo add eks https://aws.github.io/eks-charts

• Next, update the Helm repositories:

# helm repo update

• Create a namespace for the controller (optional but recommended):

# kubectl create namespace aws-load-balancer-controller

• Install the AWS Load Balancer Controller using Helm. Replace `<CLUSTER_NAME>` with the name of the Kubernetes cluster.

      # helm upgrade -i aws-load-balancer-controller eks/aws-load-balancer-controller \

        –namespace=aws-load-balancer-controller \

        –set clusterName=<CLUSTER_NAME> \

        –set serviceAccount.create=true \

        –set serviceAccount.name=aws-load-balancer-controller

            5. Verify the deployment

      # kubectl get deployment -n aws-load-balancer-controller aws-load-balancer-controller

• Deploying a nginx image and exposing it as a Clusterip service.

  # Sample Nginx deployment

  apiVersion: apps/v1

  kind: Deployment

  metadata:

    name: nginx

    labels:

      app: nginx

  spec:

    replicas: 1

    selector:

      matchLabels:

        app: nginx

    template:

      metadata:

        labels:

          app: nginx

      spec:

        containers:

        – name: nginx

          image: nginx

          ports:

          – containerPort: 80

  #SVC Exposing as clusterIP

  apiVersion: v1

  kind: Service

  metadata:

    labels:

      app: nginx

    name: nginx

  spec:

    ports:

    – port: 80

      protocol: TCP

    selector:

      app: nginx

Apply the deployment and service configuration by running the kubectl commands

    # kubectl apply -f nginx_deploy.yml

To verify the deployment run the below command

                # kubectl get deployment nginx

• Adding Ingress routes

Update the Ingress resource with AWS-specific annotations to control how the controller configures the load balancer.

For example, we can specify the load balancer type (e.g., Application Load Balancer or Network Load Balancer) and configure SSL termination, authentication, and other load balancer settings. Here we’ll be using AWS Certificate Manager (ACM) for configuring HTTPS, We need to provide the ACM arn in the annotation “alb.ingress.kubernetes.io/certificate-arn”

Here’s an example of an Ingress resource with AWS annotations:

apiVersion: networking.k8s.io/v1

kind: Ingress

metadata:

  name: my-ingress

  annotations:

    kubernetes.io/ingress.class: alb

    alb.ingress.kubernetes.io/scheme: internet-facing

    alb.ingress.kubernetes.io/certificate-arn: <acm_ssl_arn>

    alb.ingress.kubernetes.io/listen-ports: ‘[{“HTTP”: 80}, {“HTTPS”:443}]’

    alb.ingress.kubernetes.io/actions.ssl-redirect: >-

        {

            “Type”: “redirect”,

            “RedirectConfig”: {

                “Protocol”: “HTTPS”,

                “Port”: “443”,

                “Host”: “#{host}”,

                “Path”: “/#{path}”,

                “Query”: “#{query}”,

                “StatusCode”: “HTTP_301”

            }

        }

spec:

  rules:

    – host: demo-app.example.com

      http:

        paths:

          – path: /

            pathType: Prefix

            backend:

              service:

                name: nginx

                port:

                  number: 80

Once the controller is installed and configured, it will automatically create and manage AWS load balancers based on our Kubernetes resources. This means we can define and update our load balancers directly in our cluster’s YAML files, making it easier to manage our application’s network traffic.

Key benefits of managing ELBs with the AWS Load Balancer Controller:

• Simplified Configuration: The AWS Load Balancer Controller simplifies the process of creating and managing load balancers in AWS. Kubernetes manifests, such as Ingress resources, can be used to define routing rules, SSL certificates, and other load balancing configurations.
• Flexibility: We can define and update load balancers as Kubernetes resources, making it easy to scale and modify our application’s network setup.
• Automation: The controller automates the creation and management of AWS load balancers, reducing manual tasks and the risk of misconfigurations.
• Autoscaling: As your application scales, the AWS Load Balancer Controller dynamically adjusts the associated AWS resources to handle increased traffic. This ensures that your application remains highly available and responsive.
• Integration: AWS Load Balancer Controller integrates seamlessly with other AWS services, such as AWS Certificate Manager for SSL certificates and AWS Web Application Firewall for security.
• Consistency: The controller ensures that our AWS load balancers are consistent with our Kubernetes configuration, reducing the risk of drift.

Conclusion

Managing elastic load balancers for a Kubernetes cluster using the AWS load balancer controller simplifies the process of load balancer configuration and management. By integrating the controller with our cluster, we can define our load balancers as Kubernetes resources and let the controller handle the rest. This approach streamlines operations, increases automation, and ensures a consistent and reliable network infrastructure for our applications in the AWS cloud.

The post Managing ELB for a Kubernetes Cluster using AWS Load Balancer Controller appeared first on Indium.

One of the most useful features of Terraform is its ability to import existing resources into your infrastructure as code. This allows you to take control of resources that may have been created manually or by another team and bring them under the management of your infrastructure as code. However, the process of importing resources can be time-consuming and error-prone, particularly if you are dealing with many resources or complex configurations. This is where Terraformer comes in.

What is Terraformer?

Terraformer is an open-source tool written in Go Language, originally developed by Waze SRE that automates the process of importing existing resources into Terraform. It allows you to generate Terraform code from existing resources, making it easy to manage them using Terraform. Terraformer supports a wide range of cloud providers, including AWS, GCP, Azure, Kubernetes, and more. Terraformer currently supports sixteen clouds and more than fifteen providers like Datadog, Kubernetes, PagerDuty, GitHub, and more.

How does Terraformer stand apart from its competitors?

1. Terraformer differs from other competitors in a few keyways.
2. Terraformer is a command-line tool, while some of its competitors are web-based tools. This makes Terraformer more portable and easier to use in a CI/CD pipeline.
3. Terraformer eliminates the manual intervention needed in other IaC tools by automatically generating configurations after importing the infrastructure.
4. Terraformer supports a wider range of infrastructure sources than some of its competitors. Terraformer currently supports AWS, Azure, GCP, and Kubernetes, while some of its competitors only support a subset of these providers.
5. Finally, Terraformer is easier to use as it has a simpler user interface and provides more helpful documentation.

How to use Terraformer?

Using Terraformer is straightforward. First, you need to install it on your local machine. You can do this using the command-line interface (CLI) or a Docker container. Once installed, you can use Terraformer to generate Terraform code for existing resources.

To install Terraformer on a Linux machine, run the below commands:

export PROVIDER={aws}

curl -LO https://github.com/GoogleCloudPlatform/terraformer/releases/download/$(curl -s https://api.github.com/repos/GoogleCloudPlatform/terraformer/releases/latest | grep tag_name | cut -d ‘”‘ -f 4)/terraformer-${PROVIDER}-linux-amd64

By running the below command, you can check the Terraformer version.

terraformer -v

To import resources with an AWS provider, first authenticate to the AWS account in which the resource is located. To configure Terraformer with your AWS credentials, you would use the following command

terraformer configure –provider aws –credentials ~/.aws/credentials

AWS configuration, including environmental variables, a shared credentials file (~/.aws/credentials), and a shared configuration file (~/.aws/config) will be loaded by the tool by default. To use a specific profile, you can use the following command:

After authenticating to the AWS account, run terraform init against a provider.tf file to install the plugins required for your platform.

terraform {

  required_providers {

    aws = {

      source  = “hashicorp/aws”

      version = “~> 4.0”

    }

  }

}

provider “aws” {

  region = “us-east-1”

}

To import all AWS Elasticache and RDS into Terraform, use the below command:

terraformer import aws –-path-pattern=”{output}/” –compact=true –regions=eu-central-1 –resources=elasticache,rds

The above command tells Terraformer to import all the Elastic cache and RDS in the region eu-central-1 and generate Terraform code, and the –compact=true flag tells Terraformer to write all the configurations in a single file.

Terraformer also supports importing multiple resources at once, and you can use filters to import only specific resources that meet certain criteria.

For example, you can import all AWS EC2 instances that have a certain tag by using the following command:

terraformer import aws –path-pattern=”./ec2/” –compact=true –regions=eu-central-1 –resources=ec2_instance –filter=”Name=tags.NodeRole;Value-node”

The above command tells Terraformer to create Terraform code in the directory./ec2/ and import all EC2 instances with the tag Noderole=node.

By default, Terraformer separates each resource into a file that is placed in a specified service directory. Each provider may have a different default path for resource files, which is {output}/{provider}/{service}/{resource}.tf.

We can manage the resources with Terraform using the plan, apply, and remove actions now that the configuration files have been created with Terraformer.

Also Read this informative blog on How to Secure an AWS Environment with Multiple Accounts.

Benefits of using Terraformer

Achieve Infrastructure as Code: Terraform promotes the principle of infrastructure as code, where infrastructure resources are defined in a declarative language. The tool allows users to import existing resources into Terraform, making it easier to maintain a consistent and reproducible infrastructure state.

Version and manage resources: By importing resources into Terraform, users can take advantage of Terraform’s versioning and management capabilities. This includes tracking changes, applying modifications, and collaborating on infrastructure changes through version control systems.

Apply infrastructure changes: With imported resources, users can modify and apply changes to their infrastructure using Terraform. This provides a standardised and automated way to manage the lifecycle of resources, ensuring consistency and reducing the risk of manual errors.

Leverage the Terraform ecosystem: Importing resources into Terraform allows users to leverage the extensive ecosystem of Terraform providers, modules, and other tooling. This enables the use of various integrations and extensions to enhance infrastructure management and provisioning.

Conclusion

Terraformer is a valuable tool for organizations that are looking to improve the speed, efficiency, and reliability of their infrastructure deployments. By automating the process of converting existing infrastructure to Terraform configuration files, providing a consistent and repeatable way to provision infrastructure resources, and enabling organizations to track changes to infrastructure resources and to roll back changes, if necessary, Terraformer can help organizations to save time and reduce the risk of errors and disruptions.

The post Terraformer: A Powerful Tool for Importing Infrastructure into Terraform appeared first on Indium.

What is Securing a multi-account AWS environment?

Securing a multi-account AWS environment is a critical aspect of cloud engineering services as it helps ensure the safety and privacy of the data and resources hosted on AWS. A multi-account environment refers to the use of multiple AWS accounts to isolate different environments, such as development, testing, and production, to reduce the risk of accidental resource modification or deletion.

Securing a multi-account AWS environment involves implementing various security controls, such as:

• Identity and Access Management (IAM) – Implementing IAM best practices, such as the principle of least privilege, to limit access to AWS resources to only authorized users and services.

• Network Security – Implementing network security controls such as security groups, network ACLs, and VPCs to control the ingress and egress traffic between resources and the internet.

• Encryption – Using encryption for data at rest and in transit, and implementing AWS Key Management Service (KMS) to manage encryption keys.

• Monitoring and Logging – Implementing a centralized logging and monitoring solution to track and identify any unusual activities and events.

• Security Automation – Using AWS security automation tools such as AWS Config, AWS Security Hub, and AWS GuardDuty to detect and remediate security threats in real-time.

• Compliance – Ensuring that the AWS environment is compliant with industry-specific regulations and standards such as HIPAA, PCI-DSS, and GDPR.

By implementing these security controls, a multi-account AWS environment can be better protected against security threats and data breaches, enabling cloud engineering services to operate in a secure and reliable manner.

Also read:  Looking forward to maximizing ROI from Cloud Migration? Here’s how, why and when to do it.

Problem Statement

As a cloud services provider, the top 3 inquiries from large enterprises with workloads running on AWS are:

• How can I secure my multi-account AWS environment?
• How can we make sure that all accounts are complying with compliance and auditing requirements?
• How can we complete this quickly, all at once, rather than in pieces?

Even though large organisations with numerous AWS accounts have guidelines for new AWS implementations, managing and monitoring all the accounts at once is inefficient, time-consuming, and prone to security risks.

Solution

AWS Control Tower is the best solution to provision, manage, govern, and secure a multi-AWS account environment, even though there are more traditional methods of securing AWS environments using AWS IAM, Service Catalog, Config, and AWS Organizations.

Using pre-approved account configurations, Control Tower’s Account factory automates the provisioning of new AWS accounts. A landing zone that is based on best-practices blueprints is automatically created by the control tower, and guardrails are used to enable governance. The landing zone is a multi-account baseline with sound architecture that adheres to the AWS well-architected framework. Guardrails put governance regulations for operations, compliance, and security into effect.

Organizations can use Control Tower to:

• Easily create well-designed multi-account environments; and provide federated access using AWS SSO.
• Use VPC to implement network configurations.
• Create workflows for creating accounts using AWS Service Catalog
• Ensure adherence to guardrails-set rules.
• Detect security vulnerabilities automatically.

Benefits

• Beneficial for continuously growing enterprises, where there will be new additions to AWS accounts progressively.
• Helpful for large businesses with a diverse mix of engineering, operations, and development teams
• Gives a step-by-step process to customise the build and automate the creation of an AWS Landing Zone
• Prevents the use of resources in a manner inconsistent with the organization’s policies.
• Guardrails are a high-level rule in Control Tower’s AWS Config rules and helps detecting non-conformance with previously provisioned resources.
• Provides a dashboard for quick access to provisioned accounts and reports on the detective and preventive guardrails that are activated on your accounts.
• Compliance Reports detailing any resources that violate policies that have been enabled by guardrails.

In conclusion, securing a multi-account AWS environment is crucial for ensuring the confidentiality, integrity, and availability of your organization’s data and resources. By implementing proper security measures such as access controls, monitoring, and automation, you can significantly reduce the risk of security breaches and data loss.

Indium Software’s expertise in AWS security can help organizations to design and implement a comprehensive security strategy that meets their specific needs and requirements. Their team of experts can help with security assessments, audits, and ongoing monitoring to ensure that your AWS environment is continuously protected from security threats.

The post How to Secure an AWS Environment with Multiple Accounts  appeared first on Indium.

Another crucial requirement of clients on SaaS platforms is scalability. There may be peaks and troughs in traffic to the application due to expected and unexpected reasons. A seasonal increase in demand, a promotional campaign, sudden trending of a related topic, and so on can see more click-throughs than before. Being able to scale up when the demand peaks and scale down during low-demand periods is another crucial requirement to serve customers cost-effectively.

Extensibility and scalability are an integral part of the business model and therefore requires the SaaS platform to be able to perform under such extraordinary conditions too.

Must Read: 5 Best Practices While Building a Multi-Tenant SaaS Application using AWS Serverless/AWS EKS

AWS Lambda is one such solution that can help businesses scale based on need, automatically, and allows extensibility.

AWS Lambda Features That Allow Scalability

AWS Lambda, a serverless compute service, helps to manage the compute resources needed to run the code in response to events such as updating the code, changes in the state, and so on. It can also be used for extending other AWS services using custom logic or installing customized backend services requiring scalability, performance, and security. This is made possible by Lambda, which runs the code on computing infrastructure that is highly available. It also manages the administration of the compute resources, such as maintaining the server and the operating system, provisioning capacity and scaling automatically, deploying code and security patches, and monitoring and logging code.

Using Custom Logic to Extend Other AWS Services

As data is ingested and moves through the cloud AWS resources such as Amazon DynamoDB tables and Amazon S3 buckets, the application of custom logic by AWS Lambda enables computing and keeping pace with the incoming requests.

Automatic Scaling

In AWS Lambda, the code is invoked only as per need with automatic scaling to handle the spike in requests without manual intervention and limits. Within a fraction of a second of the event beginning, the code also starts running without compromising performance. Multiple instances of the code can be run due to the code remaining stateless and not needing deployment or configuration.AWS Lambda provides a cost-effective solution for extensibility and scaling as customers pay-per-use.

Check out this article to learn about the cloud on AWS: Cloud Computing On Aws

Provisioned Concurrency

Provisioned Concurrency is a feature of AWS Lambda that enables it to respond quickly to increased demand by initialing functions and keeping them hyper-ready. This feature can be leveraged to implement interactive services on the web and mobile or to access microservices with latency-sensitivity or synchronous APIs.

Scheduled Scaling

Whenever additional workload can be predicted due to an expected increase in traffic, scheduled scaling is also possible. This can be cost-effective by being activated only when required and not at other times. Another option is utilization-based scaling, where provisioned concurrency is increased according to the established utilization metrics. This is useful when demand cannot be predicted.

Customization and Extensibility with AWS Lambda

AWS Lambda’s extensibility and customization capabilities are especially in demand by SaaS customers who have migrated from on-premises solutions. While APIs and integration hooks may address this need, sophisticated customization requires custom code to be integrated with the SaaS workflows for effectiveness.

Therefore, they face challenges such as cost, isolation, and usability. AWS Lambda being serverless, it helps to overcome these challenges by scaling the compute automatically and charging only based on use. It achieves this by abstracting away and simplifying the consumption model. SaaS builders also include controls and features that allow the customization of the execution environments within their own SaaS product. As a result, SaaS owners experience greater flexibility in choosing cost-effective usability and isolation models.

Some of the customers who have successfully enriched their user experience using the extensibility and customization of AWS Lambda for SaaS include Freshworks, Segment Functions, and Netlify Functions.

Read what our AVP of cloud services has to say about the AWS Lambda services: Securing your Serverless Lambda functions

Indium Leveraging AWS Lamba for Scale and Extensibility

Indium Software is a cutting-edge solution provider with a team of AWS specialists who can help businesses migrate/modernize their applications and data on the cloud and

leverage automation to scale. Our team works closely with our customers to understand their needs for scale and extend and develop bespoke solutions to provide cost-effective and scalable solutions. In addition to workload migration and new app development on the cloud, we also help with converting monolithic applications to microservices and leverage containerization and serverless solutions such as AWS Lambda. Be it scheduled scaling or automatic scaling, Indium can tailor the right solution to keep your business agile and responsive, increase customer satisfaction, and break barriers to innovation.

The post AWS Lambda to Extend and Scale Your SaaS Application appeared first on Indium.

Keeping track of the resilience of the system helps ensure its robustness even in case of disasters and other disruptions. There are two measures used to assess the resiliency of the apps. These include:

• Recovery Time Objective (RTO): the time needed to recover from a failure
• Recovery Point Objective (RPO): in case of an accident, the maximum window of time during which the data might be lost.

Based on the needs of the business and the nature of the application, the two metrics can be measured in terms the seconds, minutes, hours, or days.

AWS Resilience Hub

With AWS Resilience Hub, the RTO and RPO objectives can be defined for each of the applications an organization runs. It facilitates assessing the applications’ configuration to ensure the requirements are met. Actionable recommendations and a resilience score help to finetune the application and track its resiliency progress over time. An AWS Management Console provides customizable single dashboard access that allows:

• Running assessments,
• Executing prebuilt tests
• Configuring alarms to determine the issues
• Alerting the operators

With AWS Resilience Hub, applications deployed by AWS CloudFormation, such as SAM and CDK, can be discovered, even across regions and in cross-account stacks. Applications can be discovered either from Resource Groups and tags or those already defined in the AWS Service Catalog AppRegistry

Check this out: Cloud Computing On Aws

Some of the benefits of AWS Resilience Hub include:

Assessment and Recommendations: AWS Resilience Hub uses AWS Well-Architected Framework best practices for resilience assessment. This helps analyze the application components and discover possible resilience weaknesses caused by:

– Incomplete infrastructure setup

– Misconfigurations

It also helps to identify additional configuration improvement opportunities. To improve the application’s resilience, Resilience Hub provides actionable recommendations.

Resilience Hub validates the Amazon Relational Database Service (RDS), Amazon Elastic File System (Amazon EFS) backup schedule, and Amazon Elastic Block Store (EBS) of the application to meet the RPO and RTO as defined in the resilience policy. If not, then it recommends appropriate improvements.

Resilience assessment facilitates recovery procedures by generating code snippets. As part of the standard operating procedures (SOPs), AWS Systems Manager creates documents for the applications. Moreover, a list of recommended Amazon CloudWatch monitors and alarms is created to enable quickly identifying any changing the application’s resilience posture on deployment.

Continuous Validation Resilience

Once the recommendations and SOPs from the resilience assessment are updated, the next step is to test and verify to ensure that the application meets the resilience targets before being released into production. AWS Fault Injection Simulator (FIS) is a fully managed service that allows Resilience Hub to run experiments on AWS to detect real-world failures, including network errors or several open connections to a database. Development teams can also integrate their resilience assessment and testing into their CI/CD pipelines using APIs available in the Resilience Hub for validating ongoing resilience. This prevents any compromise to resilience in the underlying infrastructure.

Visibility

The AWS Resilience Hub dashboard provides a holistic view of the application portfolio resilience status, enabling tracking of the resilience of applications. It also aggregates and organizes resilience events, alerts, and insights from services such as AWS Fault Injection Simulator (FIS) and Amazon CloudWatch. A resilience score generated by the Resilience Hub provides insights into the level of implementation for recommended resilience tests, recovery SOPs, and alarms. This can help measure improvements to resilience over time.

You might be interested in this: Using AWS for Your SaaS application–Here’s What You Need to Do for Data Security

Resilience Hub Best Practices On deploying an AWS partner application into production, Resilience Hub helps to track the resiliency posture of the application, notifies in case of an outage, and helps to launch the associated recovery process. For its effective implementation, the best practices include:

Step 1-Define: The first step is to identify and describe the existing AWS application that needs to be protected from disruptions and then define the resiliency goals. To form the structural basis of the application in Resilience Hub, resources need to be imported from:

– AWS CloudFormation stacks

– Terraform state files

– Resource groups

– AppRegistry

An existing application can be used to build an existing structure and then attach the resiliency policy. The policy should include information and objectives required to assess the application’s ability to recover from a disruption type, either software or hardware. The resiliency policy should include a definition of the RTO and RPO for the disruption types, which will help evaluate the application’s ability to meet the resiliency policy.

Step 2-Assessing: Run a resiliency assessment on describing the application and attaching the resiliency policy to it to evaluate the application configuration and generates a report. This report reveals how the application meets the resiliency policy goals.

Step 3-Recommendations: The Resilience Hub generates recommendations based on the assessment report that can be used to update the application and the resiliency policy. These could be regarding configurations for components, tests, alarms, and recovery SOPs. The improvement can be assessed by running another assessment and comparing the results with the earlier report. By reiterating this process, the RTO and RPO goals can be achieved.

Step 4-Validation: To measure the resiliency of the AWS resources and the time needed to recover from outages to application, infrastructure, Availability Zone, and AWS Region, run simulation tests such as failovers, network unavailable errors, stopped processes, problems with your Availability Zone, and Amazon RDS boot recovery. This can help assess the application’s ability to recover from the different outage types.

Step5-Tracking: Resilience Hub can continue to track the AWS application posture after deploying it into production. In case of an outage, it can be viewed in Resilience Hub and the associated recovery process launched.

Step 6-Recovery After Disruption: During application disruption, Resilience Hub can help detect the type of disruption and alert the operator, who can launch the SOP associated with the type for recovery.

Indium Software, an AWS partner, can help you ensure undisrupted application performance by implementing an effective AWS Resilience Hub for your applications based on your business objectives.

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This has led to the emergence of data mesh, a paradigm shift in modern data architecture that allows data to be considered a product. As a result, data architectures are being designed with distributed data around business domains with a focus on the quality of data being produced and shared with consumers.

Domain-Driven Design for Scalable Data Architecture

In the Domain Driven Design, or DDD, software design approach, the solution is divided such that the domains align with business capabilities, organizational boundaries, and software. This is a deviation from the traditional approach, where technologies are at the core of data architecture and not business domains.

Data mesh is a modern architectural pattern that can be built using a service such as AWS Lake Formation. The AWS modern data architecture allows architects and engineers to:

• Build scalable data lakes rapidly
• Leverage a broad and deep collection of purpose-built data services
• Be compliant by providing unified data access, governance, and security

Why You Need a Data Mesh

Businesses should be able to store structured and unstructured data at any scale, which can be available for different internal and external uses. Data lakes may require time and effort to ingest data and be unable to meet the varied and increasing business use cases. Often businesses try to cut costs and maximize value by planning one-time data ingestion into their data lake consuming it several times. But what they truly need is a scalable data lake architecture that scales. This adds value and provides continuous, real-time data insights to improve competitive advantage and accelerate growth.

By designing a data mesh on the AWS Cloud, businesses can experience the following benefits:

• Data sharing and consumption across multiple domains within the organization is simplified.
• Data producers can be onboarded at any time without the need for maintaining the entire data-sharing process. Data producers can continue with collecting, processing, storing, and onboarding data from their data domain into the data lake as and when needed.
• This can be done without incurring additional costs or management overhead.
• It assures security and consistency, thereby enabling external data producers also to be included and data shared with them in the data lake.
• Data insights can be gained continuously, in real-time, without disruptions

Features of AWS Data Architecture for Scalability

A data producer collects, processes, stores, and prepares for consumption. In the AWS ecosystem, the data is stored in Amazon Simple Storage Service (Amazon S3) buckets with multiple data layers if required. AWS services such as AWS Glue and Amazon EMR can be used for data processing.

AWS Lake Formation facilitates the data producer to share the processed data with the data consumer based on the business use cases. As the data produced grows, the number of consumers also increases. The earlier approach to managing this data-sharing manual is ineffective and prone to errors and delays. Developing an automated or semi-automated approach to share and manage data and access is an alternative, but also limited in effectiveness as it needs time and effort to design and build the solutions and also ensure security and governance. Over a period of time, it can become complicated and difficult to manage.

The data lake itself may become a bottleneck and not grow or scale. This will require redesigning and rebuilding the data lake to overcome the bottleneck and lead to increased utilization of cost, time, and resources.

This hurdle can be overcome using AWS Auto Scaling, which monitors applications and provides a predictable and cost-effective performance through automatic adjustment of capacity. It has a simple and powerful user interface that enables building plans for scaling resources such as Amazon ECS tasks, Amazon EC2 instances, Amazon DynamoDB tables and indexes, Amazon Aurora Replicas, and Spot Fleets. It provides recommendations for optimizing performance and costs. Users of Amazon EC2 Auto Scaling can combine it with AWS Auto Scaling to scale resources used by other AWS services as needed.

Benefits of AWS Auto Scaling

Some of the benefits of using AWS Auto Scaling include:

• Quick Setup for Scaling: A single, intuitive interface allows the setting of target utilization levels for different resources. A centralized control negates the need for navigating to other consoles.
• Improves Scaling Decisions: By building scaling plans using AWS Auto Scaling, businesses can automate the use of different resources by different groups based on demand. This helps with balancing and optimizing performance and costs. With AWS Auto Scaling, all scaling policies and targets can be created automatically based on need, adding or removing capacity in real time based on changes in demand.
• Automated Performance Management: AWS Auto Scaling helps to optimize application performance and availability, even in a dynamic environment with unpredictable and constantly changing workloads. By continuously monitoring applications, it ensures optimal performance of applications, increasing the capacity of constrained resources during a spike in demand to maintain the quality of service.
• Pay Per Use: Utilization and cost efficiencies of AWS services can be optimized as businesses pay only for the resources they need.

Indium to Enable Modern Data Architecture on AWS Ecosystem

Indium Software has demonstrated capabilities in AWS ecosystem, having delivered more than 250 data, ML, and DevOps solutions in the last 10+ years.

Our team consists of more than 370 data, ML, and DevOps consultants, 50+ AWS-certified engineers, and experienced technical leaders delivering solutions that break barriers to innovation. We work closely with our customers to deliver solutions based on the unique needs of the business.

FAQs

How can I scale AWS resources?

AWS offers different options for scaling resources.

• Amazon EC2 Auto Scaling ensures access to the correct number of Amazon EC2 instances for handling the application load.
• Application Auto Scaling API that allows defining scaling policies for automatic scaling of AWS resources. It also allows scheduling scaling actions on a one-time or recurring basis.
• AWS Auto Scaling facilitates the automatic scaling of multiple resources across multiple services.

What is a scaling plan?

The collection of instructions for scaling for different AWS resources is called a scaling plan. Two key parameters for this are resource utilization metric and incoming traffic metric.

The post Modern Data Architecture on AWS Ecosystem: Is Your Company’s Data Ecosystem Setup for Scale? appeared first on Indium.

Some of the key technologies that enable the data lakehouse to provide these benefits include:

• Layers of metadata
• Improved SQL execution enabled by new query engine designs
• optimized access for data science and machine learning tools.

Data Lakes for Improved Performance

Metadata layers track the files that can be a part of different table versions to enable ACID-compliant transactions. They support streaming I/O without the need for message buses such as Kafka), facilitating accessing older versions of the table, enforcement and evolution of schema, and validating data.

But among these features, what makes the data lake popular is its performance with the introduction of new query engine designs for SQL analysis. In addition, some optimizations include:

• Hot data caching in RAM/SSDs
• Cluster co-accessed data layout optimization
• Statistics, indexes, and other such auxiliary data structures
• Vectorized execution on modern CPUs

This makes data lakehouse performance on large datasets comparable to other popular TPC-DS benchmark-based data warehouses. Being built on open data formats such as Parquet makes access to data easy for data scientists and machine learning engineers in the lakehouse.

Indium’s capabilities with Databricks services: UX Enhancement & Cross Platform Optimization of Healthcare Application

Easy Steps to Building Databricks Data Lakehouse on AWS

As businesses increase their adoption of AI and analytics and scale up, businesses can leverage Databricks consulting services to experience the benefits of their data by keeping it simple and accessible. Databricks provides a cost-effective solution through its pay-as-you-go solution on Databricks AWS to allow the use of existing AWS accounts and infrastructure.

Databricks on AWS is a collaborative workspace for machine learning, data science, and analytics, using the Lakehouse architecture to process large volumes of data and accelerate innovation. The Databricks Lakehouse Platform, forming the core of the AWS ecosystem, integrates easily and seamlessly with popular Data and AI services such as S3 buckets, Kinesis streams, Athena, Redshift, Glue, and QuickSight, among others.

Building a Databricks Lakehouse on AWS is very easy and involves:

Quick Setup: For customers with AWS partner privileges, setting up Databricks is as simple as subscribing to the service directly from their AWS account without creating a new account. The Databricks Marketplace listing is available in the AWS Marketplace and can be accessed through a simple search. A self-service Quickstart video is available to help businesses create their first workspace.

Smooth Onboarding: The Databricks pay-as-you-go service can be set up using AWS credentials. Databricks allows the account settings and roles in AWS to be preserved, accelerating the setting up and the kick-off of the Lakehouse building.

Pay Per Use: The Databricks on AWS is a cost-effective solution as the customers have to pay based on the use of resources. The billing is linked to their existing Enterprise Discount Program, enabling them to build a flexible and scalable lakehouse on AWS based on their needs.

Try Before Signing Up: AWS customers can opt for a free 14-day trial of Databricks before signing up for the subscription. The billing and payment can be consolidated under their already-present AWS management account.

Benefits of Databricks Lakehouse on AWS

Apart from a cost-effective, flexible and scalable solution for improved management of AI and analytics workloads, some of the other benefits include:

• Supporting AWS Graviton2-based Amazon Elastic Compute Cloud (Amazon EC2) instances for 3x improvement in performance
• Exceptional price-performance ensured by Graviton processors for workloads running in EC2
• Improved performance by using Photon, the new query engine from Databricks Our Engineering team ran benchmark tests and discovered that Graviton2-based

It might be interesting to read on End-To-End ML Pipeline using Pyspark and Databricks (Part I)

Indium–A Databricks Expert for Your AI/Analytics Needs

Indium Software is a leading provider of data engineering, machine learning, and data analytics solutions. An AWS partner, we have an experienced team of Databricks experts who can build Databricks Lakehouse on AWS quickly to help you manage your AI and analytics workloads better.

Our range of services includes: Data Engineering Solutions: Our quality engineering practices optimize data fluidity from origin to destination.

BI & Data Modernization Solutions: Improve decision making through deeper insights and customized, dynamic visualization

Data Analytics Solutions: Leverage powerful algorithms and techniques to augment decision-making with machines for exploratory scenarios

AI/ML Solutions: Draw deep insights using intelligent machine learning services

We use our cross-domain experience to design innovative solutions for your business, meeting your objectives and the need for accelerating growth, improving efficiency, and moving from strength to strength. Our team of capable data scientists and solution architects leverage modern technologies cost-effectively to optimize resources and meet strategic imperatives.

Inquire Now to know more about our Databricks on AWS capabilities.

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The emergence of the PC, which made owning a computer much cheaper, and subsequently the rise of corporate data centres, which allowed organisations to store massive amounts of data, completely eclipsed these ‘time-sharing’ services.

However, in the late 1990s and early 2000s, the concept of renting access to computer power reappeared in the form of application service providers, utility computing, and grid computing. Following that, it gained traction with the introduction of software as a service (SaaS) and evolution of hyperscale cloud computing companies like Amazon Web Services (AWS).

With increasing cloud adoption, cloud computing-as-a-service is now fast emerging. Coming with several excellent features, Amazon Web Service (AWS) is a leader in cloud computing as a service. This blog explains in detail, as to why businesses should implement AWS cloud computing services.

What is Cloud Computing?

The provision and delivery of numerous services over the Internet is known as cloud computing. These resources include servers, databases software applications etc.

Cloud-based storing allows you to save files to a remote database rather than maintaining them on a local storage device. As long as an electronic device has internet access, it has access to the data as well as the software programmes hosted by the cloud. So, the user need not be at a specific location to access data or applications, which offers them seamless flexibility to work remotely.

For a variety of reasons, including cost savings, productivity, speed, performance, efficiency, and security, thus, cloud computing is being increasingly preferred by enterprises. It has grown in popularity as a result of significant advancements in virtualization and distributed computing, as well as greater access to high-speed internet.

Because the data being accessed is situated remotely in the cloud or a virtual place, cloud computing is dubbed as such.

Cloud computing offloads all the hard labour associated with crunching and processing data from your device. It also offloads the processing by massive computer clusters located thousands of miles distant in cyberspace.

Cloud computing solutions are available in both public and private versions. For a price, public cloud providers offer their services over the Internet. Private cloud services, on the other hand, cater to a limited number of customers. These services are a network system that provides hosted services. A hybrid option is also available, which includes components of both public and private services.

Cloud computing vs Traditional web hosting

A cloud service is distinguished from traditional web hosting by three main characteristics, which are:

– On-demand access to enormous amounts of computing power is available to users. Typically, it is sold by the minute or by the hour.

– It is adaptable, allowing users to have as much or as little service as they choose at any particular time.

– The provider is in charge of the entire service (the customer needs nothing but a personal computer and access to internet).

Must read: The Future Of Cloud Computing : Things To Look Out For

Cloud computing using AWS

AWS-based cloud computing provides a work advantage to developer and IT teams. It allows them to focus on their core tasks while keeping them away from involving in functional processes such as procurement, capacity planning, inventory management, maintenance etc…

Below are some of the reasons and considerations as to why businesses need to implement cloud computing for enhanced business processes.

• Change your capital investment to variable expense: Instead of investing substantially in data centres and servers before knowing how you’ll use them, you can pay just when you utilise computing resources, and only for how much you use.
• Take advantage of vast economies of scale: Cloud computing allows you to achieve lower variable costs than you could on your own. Since the cloud aggregates the data of hundreds of thousands of consumers, companies like AWS may achieve greater economies of scale, resulting in reduced pay-as-you-go costs.
• Stop speculating about capacity: Stop guessing about your infrastructure capacity requirements. When capacity decisions are made before an application is deployed, you often wind up with either expensive idle resources or constrained capacity. These issues are no longer an issue because to cloud computing. You can use as much capacity as required and scale up or scale down with just a few minutes’ notice.
• Experience increased speed– New IT resources are simply a click away in a cloud computing environment. This means you can cut the time it takes to make such resources available to your developers from weeks to minutes. Because the cost and time it takes to experiment and innovate are greatly reduced, the organization’s agility increases dramatically.
• Shift focus on priority tasks: Stop wasting money on data centres and instead focus on projects that differentiate your company, not infrastructure. Instead of the heavy labour of racking, stacking, and powering servers, cloud computing allows you to focus on your own clients.
• Implement easily to generate fast results: With only a few clicks, you can easily deploy your application in numerous areas throughout the world. This allows you to provide your clients with lower latency and a better experience at a low cost.

Benefits of Cloud Computing on AWS

Simple to use / Easy-to-use

AWS can let application providers and vendors host their applications fast and securely, irrespective of if they are existing or new SaaS-based apps. AWS’s Management Console and well-documented web service APIs easily allows to access AWS application hosting platform.

Flexible

You can choose your operating system, programming language, web application platform, database, and other services on AWS’s platform. Further, AWS provides you with a virtual environment in which you can install the requisite applications and services. This simplifies the migration of existing apps while keeping the ability to create new ones.

Cost-Effective

There are no long-term contracts or upfront obligations, and you just pay for the compute power, storage, and other resources you utilise. The AWS Economics Center has more information on comparing the costs of different hosting options with AWS.

Reliable With AWS, you get access to a scalable, reliable, and secure worldwide computing infrastructure that has been perfected over a decade as the virtual backbone of Amazon`s multibillion-dollar online business.

Performance-driven and scalable

Offering superb features like Auto Scaling and Elastic Load Balancing, AWS lets you scale up or scale down on order.

You have access to computation and storage resources when you need them, thanks to Amazon’s vast infrastructure.

Secure

To secure and fortify our infrastructure, AWS takes an end-to-end approach that includes physical, operational, and software measures. Visit the AWS Security Center for further information.

Conclusion

As cloud computing has become more widespread, a variety of models and deployment methodologies have arisen to fulfil the needs of various users. Amongst all, AWS offers you varied levels of control and flexibility to manage.

Understand the differences between Software as a Service, Infrastructure as a Service, and Platform as a Service, as well as various deployment options available. Our experts can assist you in determining which combination of services is most suited to your requirements and guide you in AWS implementation.

The post Cloud Computing On AWS appeared first on Indium.

However, rise in serverless adoption has also seen rise in security related incidents. Despite the security features offered by cloud providers, typical characteristics of serverless such as short runtime durations, volume of executions, and the dynamic and fluid nature of the server functions can make it difficult to detect, investigate and respond to a potential compromise. 

For example, the Denonia Malware, first ever known malware to attack AWS Serverless service – Lambda functions – created ripples in the serverless world. This malware written in Go Lang used a DNS over HTTPS (DoH). DoH encrypts DNS queries and sends the requests out as regular HTTPS traffic to DoH resolvers.

The primary reason for this gap is lapse in customer commitment to uphold the shared-responsibility agreement.  While AWS is full responsible for global infrastructure that runs all of AWS Cloud, under the shared-responsibility model the user maintains control over his or her content that is hosted on the Amazon infrastructure, including  Serverless functions such as AWS Lambda.  When customers fail to keep up their end of the promise, it leads to security lapses such as the Denonia malware.

Evolution in Serverless

The Denonia malware incident exposed a vulnerability that led AWS to introduce  “additional- security mechanism” to protect the Lambda functions – the Lambda function URLs –  a new feature allowing cloud builders to set up simple, dedicated application endpoints for Lambda functions.

A function URL is a dedicated HTTP(S) endpoint for your Lambda function. You can create and configure a function URL through the Lambda console or the Lambda API.

When you create a function URL, Lambda automatically generates a unique URL endpoint for you. Function URL endpoints have the following format:

https://.lambda-url..on.aws

Although Lambda functions could already be externally exposed via API gateways and load balancers, Lambda URLs let AWS users fast-track this process with minimal overhead, for straightforward use-cases such as webhook handlers.

Additionally, Lambda URLs can be used during the development and testing of Serverless applications, allowing developers to focus on core functionality and postpone dealing with validation and authorization requirements to later stages of development.

Use case for Lambda function URLs 

Description: Lambda call using function URL

Function URLs are best for use cases where you must implement a single-function microservice with a public endpoint that doesn’t require the advanced functionality of API Gateway, such as request validation, throttling, custom authorizers, custom domain names, usage plans, or caching.

For example, when you are implementing webhook handlers, form validators, mobile payment processing, advertisement placement, machine learning inference etc.

The risks of “insecure” use of AWS Lambda function URLs

Lambda function URLs may be simple, but like any other externally exposed resource in your cloud environment, it is important that they be properly secured as well. While functions residing behind an API gateway or load balancer rely on the secure configuration of these frontend services, function URLs must be independently secured, and misconfigured instances could pose an attractive target for malicious actors hoping to cause damage or gain access to sensitive data.

Below is a classic example of A function URL could be at risk of an attack under the following circumstances:

1. 1. An attacker discovers the function URL in your environment (
2. 2. It has been misconfigured to accept HTTP requests without requiring authentication.
3. 3. The function’s resource policy authorizes invocation by unauthenticated principals. This is the default setting if no authentication was configured.
4. 4. The attacker figures out how to use the function, or in other words, what arguments it accepts.

The above elaborated risk could give rise to multiple impacts to the targeted cloud environment:

1. 1. Data retrieval or manipulation– An attacker could abuse the function to query business critical data, erase it, overwrite it, or manipulate it
2. 2. Initial access or privilege escalation – If the targeted function is capable of administration purposes, such as creating new users or roles, adding users to existing groups, changing permissions, modifying policies, resetting passwords, etc., then an attacker could misuse privilege management
3. 3. Distributed Denial of service (DDoS)– By filling the customer’s regional quota for concurrent instances, an attacker could lead to Denial of Service, bringing down the customer environment.
4. 4. Denial of wallet (DoW) – By continuously invoking the function, an attacker could incur increased costs to the AWS customer.

This might interest you: AWS Glue for Serverless Data Integration Services for Analytics and Machine Learning

Being proactive rather than reactive with Lambda Functions

1. IAM authentication and authorization

In general, it is advised to always require both authentication and authorization for invocation of a Lambda function, and to host it in a private subnet within a VPC. If your function needs public internet access, for example, to query an external non-AWS API, then you should expose it through a NAT gateway in a public subnet within the same VPC.

2. CORS

If cross-origin resource sharing (CORS) configuration is enabled, by default the function URL will accept HTTP requests from any origin (domain), which is not recommended. Therefore, you should configure additional CORS constraints, such as only allowing HTTP requests from specific origins, or requests containing specific headers and HTTP methods (GET, POST, etc.)

3. Reserved concurrency

By reserving concurrency  for every function with a URL and limiting it to a maximum value, you can ensure that even if a function URL is somehow invoked repeatedly by a malicious actor attempting to cause disruption, any business impact will be limited to the specific compromised function, while unrelated operations in the same region will remain unaffected.

About Indium Software

Indium is a digital engineering leader and full spectrum integrator that helps customers embrace and navigate the cloud-native world with certainty. Regardless of where you are in your digital journey, Indium helps you to migrate/modernize your applications and data on the cloud, make sense of the data and leverage automation to scale and innovate in a secured and compliant fashion.

With deep expertise across Applications, Data & Analytics, AI, DevOps, Security and QA Indium makes technology work and accelerate business value, while adding scale and velocity to customer’s digital journey on AWS.

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These instances show that the users of AWS, a very popular SaaS platform, need to be very careful about their data security and put up appropriate security to safeguard the safety and privacy of their data.

Security Posture with AWS

AWS is ahead of the competition having cornered 32-33% of the $178 billion cloud infrastructure services market in 2021. Apart from its other benefits, AWS provides its own security with network architecture and data centers to protect enterprise data, information, devices, identities, and applications. It helps businesses meet security and compliance requirements regarding data locality, confidentiality, and protection, and with our comprehensive services and features.

AWS allows the automation of security to enable scaling and innovation of business and as a SaaS solution, users also benefit from lower costs as they pay only as per use.

Some of the features of the AWS security include:

Scalability, Visibility, Control: AWS empowers businesses to determine their data governance policies including where to store it, who has access to it, the resources it will consume at any given time, and so on. Identity and access controls with continuous monitoring provide near real-time information to ensure access to the right resources at all times. The solution can be integrated with the existing solutions.

Integrated Services for Automation and Risk Reduction: AWS facilitates automating security tasks to reduce the risk of errors due to human configuration errors.

Ensuring Highest Standards for Privacy and Data Security: The AWS data centers are monitored by security experts 24×7. Further, the data is encrypted before flowing through the AWS global network with additional encryption layers. These include customer or service-to-service TLS connections and VPC cross-region peering traffic, which are provided for extra protection.

Security and Compliance Controls: Third-party validation helps ensure that the AWS is compliant with most global regulatory requirements encompassing retail, finance, healthcare, and government, among others.

Misconfigurations Leading to AWS Breaches

Despite the in-built security feature and constant monitoring, why then do businesses that host their services on AWS face security breaches?

The vulnerability is often due to misconfiguration which leaves the applications prone to hacking. The most common causes for vulnerabilities include

Problem #1 Insufficient Permissions and Encryptions: Simple Storage Service (S3) infrastructure, also called S3 buckets, in AWS allows users to store and retrieve data by creating one wherever they want. This allows them to upload the data fast and cost-effectively. However, unless it is configured as private and permissions provided only to authorized users, it can be made public easily.

Problem #2 Making Amazon Machine Images (AMIs) Public by Mistake: Amazon Machine Images (AMIs), needed to launch an Amazon Elastic Compute Cloud (EC2) instance and replicate an existing solution for elastic cloud-based storage, can also be accidentally made public. Ensuring that it is set to private is essential for a secure system.

Problem #3 Identity and Access Management (IAM): Incorrect configuration of Identity and Access Management (IAM) is yet another reason why security can be compromised. Ensure that only the authorized users have permission for maintaining enterprise security protocols.

Problem #4 CloudTrail Logging: Amazon CloudTrail is a log of APIs recording all the calls made against their account and depositing them in the relevant S3 bucket. Often this is disabled because of which the source of API requests will not be known. When it is not enabled, the organization may not realize when there is a DDoS attack or where it originates.

Problem #5 S3 Buckets Logging: Disabling or not enabling S3 bucket logs makes security weaknesses potentially serious in your AWS account(s). Enable and review periodically to improve security.

Problem #6 Insufficient IP addresses Enabled within a Virtual Private Cloud (VPC): All who need access should be given it within the Virtual Private Cloud (VPC) infrastructures with enough IP addresses. While too many open IP addresses could pose a problem, not enough will prevent those who need to get in from accessing the apps.

Problem #7 Network Access Control (NACL) Allowing Uncontrolled Inbound Traffic: An optional layer, the Network Access Control list (NACL) manages the traffic flow in a subnet in a network such as a VPC or VPN. This too, when not configured properly, is a security concern.

Indium for a Secure AWS Hosting

The key to a secure AWS environment is in the proper configuration to ensure data security and privacy. India, Software, a leading provider of data, development, and security solutions, can help you leverage the flexibility and scalability of the AWS platform by configuring and enabling as required.

Indium is an AWS Partner that ensures that businesses leverage the speed of digital transformation by leveraging the underlying capabilities of the AWS cloud platform and maximize its services. Indium provides a secure solution while enabling you to:

● Migrate/modernize your applications and data on the cloud

● Leverage your data automation to scale and innovate in a secure, reliable, and compliant fashion

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