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Amazon EC2

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General purpose instances

Compute optimized instances

Memory optimized instances

Storage optimized instances

Use AWS Secrets Manager and enable automatic rotation for the credentials.

Use AWS Systems Manager Parameter Store and implement a custom rotation process.

Store the credentials in an Amazon S3 bucket encrypted with AWS KMS. Update the application to retrieve credentials from S3.

Encrypt the credentials file using AWS KMS and store it on an encrypted EBS volume attached to each EC2 instance.

Deploy a transit gateway in each Region and establish inter-Region peering connections between all transit gateways.

Implement AWS Global Accelerator with UDP listeners and configure endpoint groups in each Region.

Use Amazon CloudFront with UDP support enabled and configure an origin server in each Region.

Create a VPC peering mesh connecting all Regions and enable UDP optimization for each VPC.

Connect the IoT sensors to AWS IoT Core. Configure a rule to trigger an AWS Lambda function that transforms the data into Parquet format and stores it in Amazon S3. Use AWS Glue to catalog the data and Amazon Athena for querying, with Amazon QuickSight for visualization.

Deploy the legacy application on Amazon ECS with Fargate to process sensor data. Convert the XML files into a relational format and load them into Amazon Redshift for analysis.

Set up an Amazon Kinesis Data Firehose to ingest sensor data directly. Use Kinesis Data Analytics to convert the data into JSON and store it in Amazon DynamoDB. Use Amazon OpenSearch for dashboards.

Install AWS Snowcone devices in vehicles to collect sensor data locally. Periodically ship the devices to AWS and import the data into Amazon RDS for centralized analysis.

Create an AWS CodeCommit repository for each package that the data scientists need to access. Configure code synchronization between the npm registry and the CodeCommit repository. Create a VPC endpoint for CodeCommit.

Create a NAT gateway in the VPC. Configure VPC routes to allow access to the internet with a network ACL that allows access to only the npm registry endpoint.

Create a NAT instance in the VPC. Configure VPC routes to allow access to the internet. Configure SageMaker notebook instance firewall rules that allow access to only the npm registry endpoint.

Create an AWS CodeArtifact domain and repository. Add an external connection for public:npm to the CodeArtifact repository. Configure the npm client to use the CodeArtifact repository. Create a VPC endpoint for CodeArtifact.

Use the PutClusterCapacityProviders API operation to update the ECS cluster with the FARGATE and FARGATESPOT capacity provider strategies. Set FARGATE as the default provider with a base value and FARGATESPOT as the secondary provider for cost optimization.

Use the CreateCapacityProvider API operation to associate the ECS cluster with the FARGATE and FARGATESPOT capacity provider strategies. Configure FARGATE as the primary provider with a base value and FARGATESPOT as the secondary provider.

Use the PutClusterCapacityProviders API operation to update the ECS cluster with the FARGATESPOT and FARGATE capacity provider strategies. Set FARGATESPOT as the default provider for maximum cost savings and FARGATE as the failover provider.

Use the CreateCapacityProvider API operation to associate the ECS cluster with the FARGATESPOT and FARGATE capacity provider strategies. Configure FARGATESPOT as the primary provider and FARGATE as the secondary provider.

Separately package the machine learning library with each Lambda function in individual .zip files, maintaining independent deployments.

Create a shared Lambda layer containing the machine learning library and attach it to both Lambda functions.

Merge both Lambda functions into a single function with conditional logic, deployed as one .zip archive.

Store the machine learning library in an S3 bucket and configure Lambda functions to download it during execution.