AWS Lambda

AWS Lambda is a serverless compute service that runs AWS customers code in response to events and automatically manages the underlying compute resources. It can be used to extend other AWS services with custom logic, or create, back-end services that operate at AWS scale, performance, and security. AWS Lambda can automatically run code in response to multiple events, such as HTTP requests via Amazon API Gateway, modifications to objects in Amazon S3 buckets, table updates in Amazon DynamoDB, and state transitions in AWS Step Functions.

  • Serverless computing allows users to build and run applications and services without thinking about servers. With serverless computing, the application still runs on servers, but all the server management is done by AWS.
  • AWS Lambda executes customers code only when needed and scales automatically, from a few requests per day to thousands per second.
  • It runs customers code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. 
  • It can be used to build serverless applications composed of functions that are triggered by events and automatically deploy them using CodePipeline and AWS CodeBuild
AWS Lambda

Amazon Lambda Benefits

AWS Lambda automatically runs users code without requiring them to provision or manage infrastructure. Just write the code and upload it to Lambda either as a ZIP file or container image.

AWS Lambda automatically scales users application by running code in response to each event. Users code runs in parallel and processes each trigger individually, scaling precisely with the size of the workload, from a few requests per day, to hundreds of thousands per second.

Using AWS Lambda, customers only pay for the compute time they consume. Customers are charged for every millisecond code they executes and the number of times the code is triggered. 

Using AWS Lambda, users can optimize their code execution time by choosing the right memory size for your function. They can also keep the functions initialized and hyper-ready to respond within double digit milliseconds by enabling Provisioned Concurrency.

Lambda Concepts

Using Lambda, AWS customers can run functions to process events. To send events to their function, invoke it using the Lambda API, or configure an AWS service or resource to invoke it.


function is a resource that users can invoke to run their code in Lambda. A function has code to process the events that pass into the function or that other AWS services send to the function.


When invoke or view a function, users can include a qualifier to specify a version or alias. A version is an immutable snapshot of a function’s code and configuration that has a numerical qualifier. For example, my-function:1. An alias is a pointer to a version that can update to map to a different version, or split traffic between two versions

Execution environment

An execution environment provides a secure and isolated runtime environment for Lambda function. An execution environment manages the processes and resources that are required to run the function. The execution environment provides lifecycle support for the function and for any extensions associated with the function.

Deployment package

User can deploy Lambda function code using a deployment package. Lambda supports two types of deployment packages:

  • A .zip file archive that contains users function code and its dependencies. Lambda provides the operating system and runtime for users function.

  • A container image that is compatible with the Open Container Initiative (OCI) specification. By adding their function, users can code and dependencies to the image. Users need to include the operating system and a Lambda runtime.


A Lambda layer is a .zip file archive that contains libraries, a custom runtime, or other dependencies. By using a layer users can distribute a dependency to multiple functions. Instead of using layers with container images, users can package the preferred runtime, libraries, and other dependencies into the container image when  building the image.


The runtime provides a language-specific environment that runs in an execution environment. The runtime relays invocation events, context information, and responses between Lambda and the function.

  • Users can use runtimes that Lambda provides, or build their own. If the package code is a .zip file archive, it needs to configure the function to use a runtime that matches the programming language.
  • For a container image, users need to include the runtime when building the image.

Lambda extensions enable users to augment the functions. Users can choose from a broad set of tools that AWS Lambda Partners provides, or create their own Lambda extensions.

  • An internal extension runs in the runtime process and shares the same lifecycle as the runtime.
  • An external extension runs as a separate process in the execution environment. The external extension is initialized before the function is invoked, runs in parallel with the function’s runtime, and continues to run after the function invocation is complet

An event is a JSON-formatted document that contains data for a Lambda function to process. The runtime converts the event to an object and passes it to users function code. When invoke a function, users determine the structure and contents of the event.


Concurrency is the number of requests that users function is serving at any given time. When the function is invoked, Lambda provisions an instance of it to process the event. When the function code finishes running, it can handle another request. If the function is invoked again while a request is still being processed, another instance is provisioned, increasing the function’s concurrency.

Concurrency is subject to quotas at the AWS Region level. Users can configure individual functions to limit their concurrency, or to enable them to reach a specific level of concurrency. 


trigger is a resource or configuration that invokes a Lambda function. This includes AWS services that you can configure to invoke a function, applications that users develop, and event source mappings. An event source mapping is a resource in Lambda that reads items from a stream or queue and invokes a function.

Lambda Features

AWS Lambda allows users to add custom logic to AWS resources such as Amazon S3- A great platform to store any kind of data ( and Amazon DynamoDB tables, making it easy to apply compute to data as it is enters or moves through the cloud, to run their code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs.

  • It can be used to build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB, process streaming data stored in Kinesis, or create back end that operates at AWS scale, performance, and security. 

AWS customer can use any third party library or even native ones. With that they can package any code (frameworks, SDKs, libraries, and more) as a Lambda Layer and manage and share them easily across multiple functions.

  • Lambda natively supports Java, Go, PowerShell, Node.js, C#, Python, and Ruby code, and provides a Runtime API which allows customers to use any additional programming languages to author their functions

Using AWS Lambda users can create new back-end services for the applications that are triggered on-demand using the Lambda API or custom API endpoints built using Amazon API Gateway.

  • By using Lambda to process custom events instead of servicing these on the client, users can avoid client platform variations, reduce battery drain, and enable easier updates.

A destination is an AWS resource that receives invocation records for a function. For asynchronous invocation, users can configure Lambda to send invocation records to a queue, topic, function, or event bus.

  • Users able to configure separate destinations for successful invocations and events that failed processing. The invocation record contains details about the event, the function’s response, and the reason that the record was sent.

Use concurrency settings to ensure that the production applications are highly available and highly responsive. To prevent a function from using too much concurrency, and to reserve a portion of users account’s available concurrency for a function, use reserved concurrency.

  • Reserved concurrency splits the pool of available concurrency into subsets. A function with reserved concurrency only uses concurrency from its dedicated pool.

Provisioned Concurrency gives greater control over function start time for any application that is using AWS Lambda, and it gives greater control over the performance of clients serverless application. 

  • Provisioned Concurrency keeps functions initialized and hyper-ready to respond in double-digit milliseconds. 
  • Users are able to increase the level of concurrency during times of high demand and lower it, or turn it off completely, when demand decreases.

AWS Lambda allows code to securely access other AWS services through its built-in AWS SDK and integration with AWS Identity and Access Management (IAM). AWS Lambda runs users code within a VPC by default.

  • Users can optionally configure AWS Lambda to access resources behind their own VPC, allowing them to leverage custom security groups and network access control lists to provide.

Code Signing for AWS Lambda offers trust and integrity controls which allow users to verify that only unaltered code published by approved developers is deployed in your Lambda functions. Simply create digitally signed code artifacts and configure the Lambda functions to verify the signatures at deployment.

  • This helps increase the speed and agility for development, which includes large development teams, while enforcing high security standards.

AWS Lambda supports packaging and deploying functions as container images, making it easy for customers to build Lambda based applications by using familiar container image tooling, workflows, and dependencies. Customers also benefit from the operational simplicity, automatic scaling with sub-second startup times, high availability, native integrations with 140 AWS services, and pay for use billing model offered by AWS Lambda.

  • Enterprise customers can use a consistent set of tools with both their Lambda and containerized applications for central governance requirements such as security scanning and image signing.

With Amazon Elastic File System (EFS) for AWS Lambda, users can securely read, write, and persist large volumes of data at low latency, at any scale. EFS for Lambda is ideal for building machine learning applications or loading large reference files or models, processing or backing up large amounts of data, hosting web content, or sharing files between serverless applications and instance or container based applications.

Users can coordinate multiple AWS Lambda functions for complex or long-running tasks by building workflows with AWS Step Functions.

  • Step Functions lets users define workflows that trigger a collection of Lambda functions using sequential, parallel, branching, and error-handling steps. Using Step Functions and Lambda, users can build stateful, long-running processes for applications and backends.

RDS Proxy efficiently manages thousands of concurrent database connections to relational databases, making it easy to build highly scalable, secure, Lambda-based serverless applications that need to interact with relational databases.

  • RDS Proxy offers support for MySQL and Aurora. Users can use RDS Proxy for serverless applications through the Amazon RDS console or through the AWS Lambda console.

To process items from a stream or queue, users can create an event source mapping. An event source mapping is a resource in Lambda that reads items from an Amazon Simple Queue Service (Amazon SQS) queue, an Amazon Kinesis stream, or an Amazon DynamoDB stream, and sends the items to your function in batches.

  • Event source mappings maintain a local queue of unprocessed items and handle retries if the function returns an error or is throttled.
  • Users can configure an event source mapping to customize batching behavior and error handling, or to send a record of items that fail processing to a destination.

With Lambda@Edge, AWS Lambda can run any code across AWS locations globally in response to Amazon CloudFront events, such as requests for content to or from origin servers and viewers.

  • This makes it easier to deliver richer, more personalized content to the end users with lower latency.

AWS Lambda invokes code only when needed and automatically scales to support the rate of incoming requests without requiring users to configure anything. There is no limit to the number of requests code can handle. AWS Lambda typically starts running code within milliseconds of an event.

  • Since Lambda scales automatically, the performance remains consistently high as the frequency of events increases.
  • Since the code is stateless, Lambda can start as many instances of it as needed without lengthy deployment and configuration delays.

When users invoke a function, they can choose to invoke it synchronously or asynchronously. With synchronous invocation, users need to wait for the function to process the event and return a response. With asynchronous invocation, Lambda queues the event for processing and returns a response immediately.

  • For asynchronous invocations, Lambda handles retries if the function returns an error or is throttled. To customize this behavior, users can configure error handling settings on a function, version, or alias. 

When creating a function in the Lambda console, users can choose to start from scratch, use a blueprint, use a container image, or deploy an application from the AWS Serverless Application Repository.

  • A blueprint provides sample code that shows how to use Lambda with an AWS service or a popular third-party application. Blueprints include sample code and function configuration presets for Node.js and Python runtimes.
  • Blueprints are provided for use under the Creative Commons Zero license. They are available only in the Lambda console.

AWS Lambda extensions allow users to easily integrate Lambda with their favorite tools for monitoring, observability, security, and governance. Lambda extensions run within Lambda’s execution environment which is where the function code is executed.

  • With Lambda extensions simply capture fine grained diagnostic information and send function logs, metrics, and traces to a location of users choice.
  • Integrate security agents within Lambda’s execution environment, all with no operational overhead and minimal impact to the performance of the functions.

AWS Lambda manages all the infrastructure to run the code on highly available, fault-tolerant infrastructure, freeing you to focus on building differentiated back-end services. With Lambda,  updating the underlying OS is not necessary when a patch is released, or resizing or adding new servers as the usage grows.

  • AWS Lambda seamlessly deploys the code, does all the administration, maintenance, and security patches, and provides built-in logging and monitoring through Amazon CloudWatch.

Lambda has built-in fault tolerance. AWS Lambda maintains compute capacity across multiple Availability Zones in each region to help protect users code against individual machine or data center facility failures. Both AWS Lambda and the functions running on the service provide predictable and reliable operational performance.

  • AWS Lambda is designed to provide high availability for both the service itself and for the functions it operates. There are no maintenance windows or scheduled downtimes.

AWS Lambda Permissions

Customers can use AWS Identity and Access Management (IAM) to manage access to the Lambda API and resources like functions and layers. For users and applications in customers account that use Lambda, they manage permissions in a permissions policy that customers can apply to IAM users, groups, or roles. 

  • A Lambda function has a policy called an execution role, which grants permission to access AWS services and resources. In order to access this function Amazon CloudWatch Logs for log streaming is neccessery.
  • Lambda also uses the execution role to get permission to read from event sources when clients use an event source mapping to trigger your function.
  • Useing resource-based policies AWS customers can give permission to other accounts and AWS services permission to use your Lambda resources. Lambda resources include functions, versions, aliases, and layer versions


Resource-Based Policies

Resource-based policies let clients grant usage permission to other accounts on a per-resource basis. It also can be uses as a resource-based policy to allow an AWS service to invoke a function. AWS clients can grant an account permission to invoke or manage a function To grant permissions to another AWS account, specify the account ID as the principal.

  • Resource-based policies let customers grant usage permission to other accounts on a per-resource basis, to allow AWS service those accounts to invoke the customers function.
    • Resource-based policies apply to a single function, version, alias, or layer version. They grant permission to one or more services and accounts.
    • The resource-based policy grants permission for the other account to access the function, but doesn’t allow users in that account to exceed their permissions.
  • Customers can grant an account permission to invoke or manage a function, and add multiple statements to grant access to multiple accounts, or let any account invoke their function. 
  • To limit access to a user, group, or role in another account, customers needs specify the full ARN of the identity as the principal.
  • Customers able to create one or more aliases for their AWS Lambda function. A Lambda alias is like a pointer to a specific Lambda function version. Users can access the function version using the alias ARN.


Execution Role


An AWS Lambda function’s execution role grants it permission to access AWS services and resources. AWS customers provide this role when they create a function, and Lambda assumes the role when the function is invoked.

  • Customers can create an execution role for development that has permission to send logs to Amazon CloudWatch and upload trace data to AWS X-Ray.
  • AWS Lambda allows customers to add or remove permissions from a function’s execution role at any time, or add a permissions for any services that the function calls with the AWS SDK, and for services that Lambda uses to enable optional features.
  • Managed Policies for Lambda Features 
    • AWSLambdaBasicExecutionRole:– Permission to upload logs to CloudWatch. 
    • AWSLambdaKinesisExecutionRole:– Permission to read events from an Amazon Kinesis data stream or consumer. 
    • AWSLambdaDynamoDBExecutionRole:– Permission to read records from an Amazon DynamoDB stream. 
    • AWSLambdaSQSQueueExecutionRole:– Permission to read a message from an Amazon Simple Queue Service (Amazon SQS) queue. 
    • AWSLambdaVPCAccessExecutionRole:– Permission to manage elastic network interfaces to connect your function to a VPC. 
    • AWSXRayDaemonWriteAccess:– Permission to upload trace data to X-Ray.


Resources and Conditions

Each API action supports a combination of resource and condition types that varies depending on the behavior of the action. Every IAM policy statement grants permission to an action that’s performed on a resource. When the action doesn’t act on a named resource, or when one grant permission to perform the action on all resources, the value of the resource in the policy is a wildcard (*).

  • Conditions are an optional policy element that applies additional logic to determine if an action is allowed. For common conditions supported by all actions, Lambda defines condition types that can be used to restrict the values of additional parameters on some actions.
  • The Condition element (or Condition block) lets customers specify conditions for when a policy is in effect. The Condition element is optional. In the Condition element, customers build expressions in which they use condition operators (equal, less than, etc.) to match the condition keys and values in the policy against keys and values in the request context.
  • Customers can use the Condition element of a JSON policy to test specific conditions against the request context. 
  • When a request is submitted, AWS evaluates each condition key in the policy returns a value of truefalsenot present, and occasionally null (an empty data string). 


User Policies

Lambda provides managed policies that grant access to Lambda API actions and, in some cases, access to other services used to develop and manage Lambda resources. Lambda updates the managed policies as needed, to ensure that AWS client users have access to new features when they’re released. Customers can use identity-based policies, that apply to users directly, or to groups and roles that are associated with a user, in order  to grant users in their account access to Lambda. They can also grant users in another account permission to assume a role in the account and access the Lambda resources.

  • AWSLambdaFullAccess:– Grants full access to AWS Lambda actions and other services used to develop and maintain Lambda resources.
  • AWSLambdaReadOnlyAccess:– Grants read-only access to AWS Lambda resources.
  • AWSLambdaRole:– Grants permissions to invoke Lambda functions.

AWS customers can use cross-account roles to give accounts that they trust access to Lambda actions and resources. Using resource-based policies is a better option to grant permission to invoke a function or use a layer.


Permissions Boundaries


When an application created in the AWS Lambda console, Lambda applies a permissions boundary to the application’s IAM roles. The permissions boundary limits the scope of the execution role that the application’s template creates for each of its functions, and any roles that the customer add to the template.

  • The permissions boundary prevents users with write access to the application’s Git repository from escalating the application’s permissions beyond the scope of its own resources.
  • The application templates in the Lambda console include a global property that applies a permissions boundary to all functions that they create.
  • The role that AWS CloudFormation assumes to deploy the application enforces the use of the permissions boundary. That role only has permission to create and pass roles that have the application’s permissions boundary attached.
  • An application’s permissions boundary enables functions to perform actions on the resources in the application.
  • To access other resources or API actions, customers need to expand the permissions boundary to include those resources.
    • Permissions boundary – Extend the application’s permissions boundary when you add resources to your application, or the execution role needs access to more actions.
    • Execution role – Extend a function’s execution role when it needs to use additional actions.
    • Deployment role – Extend the application’s deployment role when it needs additional permissions to create or configure resources.

Compute Environments

Job queues are generally mapped to one or more compute environments. The compute environments contain the Amazon ECS container instances that are used to run containerized batch jobs. Within a job queue, the associated compute environments each have an order that is used by the scheduler to determine where to place jobs that are ready to be executed. 

  • If the first compute environment has free resources, then the job is scheduled to a container instance within that compute environment. 
  • If the compute environment is unable to provide a suitable compute resource, the scheduler attempts to run the job on the next compute environment.
AWS Lambda
Unmanaged Compute Environments environment

Unmanaged Compute Environments environment, in this case customers are responsible for managing their own compute resources. 

  • Customers need to make sure that the AMI in use for their compute resources meets the Amazon ECS container instance AMI specification.
  • Once the unmanaged compute environment is created, customers can use the DescribeComputeEnvironments API operation to view the compute environment details. 
      • Find the Amazon ECS cluster that is associated with the environment and then manually launch your container instances into that Amazon ECS cluster.
Managed compute environments

Managed compute environments allow customers to describe their business requirements. In a managed compute environment, AWS Batch manages the capacity and instance types of the compute resources within the environment, based on the compute resource specification that they define when they create the compute environment.

  • AWS customers have two choices to use Amazon EC2: On-Demand Instances or Spot Instances.
  • Managed compute environments launch Amazon ECS container instances into the VPC and subnets that the clients specify when they created the compute environment.

Managing AWS Lambda functions

 Basic function settings include the description and the execution role that you specify when you create a function in the Lambda console. Environment variables are always encrypted at rest, and can be encrypted client-side as well. Use environment variables to make the function code portable by removing connection strings, passwords, and endpoints for external resources. Versions and aliases are secondary resources that you can create to manage function deployment and invocation. Using layers AWS customers can manage the function’s dependencies independently and keep the deployment package small, and share their libraries with other customers and use publicly available layers with the functions.



Oracle® Database is a relational database management system developed by Oracle. Amazon RDS makes it easy to set up, operate, and scale Oracle Database deployments in the cloud. With Amazon RDS, you can deploy multiple editions of Oracle Database in minutes with cost-efficient and re-sizable hardware capacity. Amazon RDS frees you up to focus on application development by managing time-consuming database administration tasks including provisioning, backups, software patching, monitoring, and hardware scaling.

  • Amazon RDS for Oracle DB Instances can be provisioned with either standard storage or Provisioned IOPS storage. 
  • Amazon RDS Provisioned IOPS is a storage option designed to deliver fast, predictable, and consistent I/O performance, and is optimized for I/O-intensive, transactional (OLTP) database workloads. 
  • In addition to that, the easy to use replication enhanced availability and reliability for production workloads. Using the Multi-AZ deployment option  customers can run mission critical workloads with high availability and built-in automated fail-over from their primary database to a synchronously replicated secondary database in case of a failure. 


Environment Variables

Environment variables can be used  to store secrets securely and adjust your function’s behavior without updating code. An environment variable is a pair of strings that are stored in a function’s version-specific configuration. 

  • The Lambda runtime makes environment variables available to customers code and sets additional environment variables that contain information about the function and invocation request.
  • By specifying a key and value, AWS customers can set environment variables on the unpublished version of their function. When you publish a version, the environment variables are locked for that version along with other version-specific configurations.
  • Lambda stores environment variables securely by encrypting them at rest. Customers can configure Lambda to use a different encryption key, encrypt environment variable values on the client side, or set environment variables in an AWS CloudFormation template with AWS Secrets Manager



Lambda Function Versions is to manage the deployment of AWS Lambda functions. Customers can publish a new version of a function for beta testing without affecting users of the stable production version. The system creates a new version of clients Lambda function each time that they publish the function. The new version is a copy of the unpublished version of the function. The function version includes:

  • The function code and all associated dependencies.
  • The Lambda runtime that executes the function.
  • All of the function settings, including the environment variables.
  • A unique Amazon Resource Name (ARN) to identify this version of the function.

While publishing a version the code and most of the settings are locked to ensure a consistent experience for users of that version. The maximum number an alias point to Lambda function versions is two, and these versions need  to meet the following criteria:

  • Both versions must have the same IAM execution role.
  • Both versions must have the same dead-letter queue configuration, or no dead-letter queue configuration.
  • Both versions must be published. The alias cannot point to $LATEST



Using Amazon Virtual Private Cloud (Amazon VPC), customers  can create a private network for resources such as databases, cache instances, or internal services. They can configure a function to connect to private subnets in a virtual private cloud (VPC) in their account. 

  • While connecting a function to a VPC, Lambda creates an elastic network interface for each combination of security group and subnet in the function’s VPC configuration.
  • Multiple functions connected to the same subnets share network interfaces, so connecting additional functions to a subnet that already has a Lambda-managed network interface is much quicker.
  • If the Functions created are not active for a long period of time, Lambda reclaims its network interfaces, and the function becomes Idle. However, Invoking an idle function will reactivate it.


To give a function access to the internet route outbound traffic to a NAT gateway in a public subnet, internet access from a private subnet  required for network address translation (NAT). The NAT gateway has a public IP address and can connect to the internet through the VPC’s internet gateway



A layer is a ZIP archive that contains libraries, a custom runtime, or other dependencies. It can be used to configure the Lambda function to pull in additional code and content in the form of layers. Layers allow customers to keep their deployment package small to make development easier. 

  • For Node.js, Python, and Ruby functions, customers can develop their function code in the Lambda console as long as they keep their deployment package under 3 MB.
    • A function can use up to 5 layers at a time. The total unzipped size of the function and all layers may not exceed the unzipped deployment package size limit of 250 MB.
  • Customers can create their own layers, or use layers published by AWS and other AWS customers. Layers support resource-based policies for granting layer usage permissions to specific AWS accounts, AWS Organizations, or all accounts


Database Access 

Amazon RDS Proxy help improve applications to pool and share database connections,  improve scalability, and makes database resilient from failures by automatically connecting to a standby DB instance while preserving application connections.

  • A database proxy manages a pool of database connections and relays queries from a function. This enables a function to reach high concurrency levels without exhausting database connections. 
  • Using the Lambda console customers can create Amazon Relational Database Service (Amazon RDS) database proxy for their function.
  • RDS Proxy also allows customers to enforce AWS IAM (Identity and Access Management) authentication to databases, and securely store credentials in Secrets Manager. RDS Proxy is fully compatible with MySQL and can be enabled for most applications with no code change



A Lambda alias is a pointer to a specific Lambda function version. It can be used to create one or more aliases for AWS clients Lambda function. Users can access the function version using the alias ARN.

  • Each alias has a unique ARN. An alias can only point to a function version, not to another alias.
  • Event sources like Amazon S3 invoke the Lambda function. These event sources maintain a mapping that identifies the function to invoke when events occur.
  • When using a resource-based policy to give service, resource, or account access to a function, the scope of that permission depends on whether customers applied it to an alias (version), or to the function.

Using routing configuration on an alias AWS clients can send a portion of traffic to a second function version.

  • By configuring the alias to send most of the traffic to the existing version, and only a small percentage of traffic to the new version, customers can reduce the risk of deploying a new version.

There are two ways to determine the Lambda function version to configure traffic weights between two function versions, which has been invoked:

  • CloudWatch Logs:– Lambda automatically emits a START log entry that contains the invoked version ID to CloudWatch Logs for every function invocation.
  • Response payload (synchronous invocations):– Responses to synchronous function invocations include an x-amz-executed-version header to indicate which function version has been invoked

AWS Lambda Applications

AWS Lambda logo

An AWS Lambda application is a combination of Lambda functions, event sources, and other resources that work together to perform tasks. users can use AWS CloudFormation and other tools to collect application’s components into a single package that can be deployed and managed as one resource. Applications make Lambda projects portable and enable users to integrate with additional developer tools, such as AWS CodePipeline, AWS CodeBuild, and the AWS Serverless Application Model command line interface (SAM CLI).

The AWS Serverless Application Repository provides a collection of Lambda applications that can be deployed in user account with a few clicks. The repository includes both ready-to-use applications and samples that can  be used as a starting point for user own projects. 

AWS CloudFormation enables you to create a template that defines your application’s resources and lets users manage the application as a stack. If any part of an update fails, AWS CloudFormation automatically rolls back to the previous configuration. With AWS CloudFormation parameters, users can create multiple environments for the application from the same template. AWS SAM extends AWS CloudFormation with a simplified syntax focused on Lambda application development.

The AWS CLI and SAM CLI are command line tools for managing Lambda application stacks. In addition to commands for managing application stacks with the AWS CloudFormation API, the AWS CLI supports higher-level commands that simplify tasks like uploading deployment packages and updating templates. The AWS SAM CLI provides additional functionality, including validating templates and testing locally.

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AWS Lambda is a serverless compute service that runs AWS customers code in response to events and automatically manages the underlying compute resources. It can be used to extend other AWS services with custom logic, or create, back-end services that operate at AWS scale, performance, and security. AWS Lambda can automatically run code in response to multiple events, such as HTTP requests via Amazon API Gateway, modifications to objects in Amazon S3 buckets, table updates in Amazon DynamoDB, and state transitions in AWS Step Functions.