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AWS Lambda

Information Technology > Web platform development

Description

AWS Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It automatically scales applications by running code in response to events, such as changes in data or user requests, and only charges for the compute time consumed. With AWS Lambda, you can integrate with other AWS services like S3, DynamoDB, and API Gateway to build event-driven architectures. It supports various programming languages and provides features like environment variables, versioning, and layers for code reuse. AWS Lambda is ideal for building scalable, cost-effective applications, enabling developers to focus on writing code while AWS handles the infrastructure management, scaling, and execution.

Stack

Serverless

Expected Behaviors

✎
LEVEL 1

Fundamental Awareness

At the fundamental awareness level, individuals are expected to have a basic understanding of AWS Lambda and its core concepts. They can identify key features and use cases, navigate the AWS Management Console, and recognize the pricing model. This level involves familiarization with the terminology and the ability to describe the primary functions of AWS Lambda.

🌱
LEVEL 2

Novice

Novices can create simple AWS Lambda functions using the console and configure basic execution roles. They can manually trigger functions and monitor their execution using CloudWatch. At this stage, they begin to apply their knowledge practically, gaining hands-on experience with AWS Lambda's basic functionalities and understanding its operational environment.

🌍
LEVEL 3

Intermediate

Intermediate users can integrate AWS Lambda with other AWS services like S3 and DynamoDB, manage function versions, and optimize performance. They understand how to implement environment variables and handle permissions more effectively. This level involves a deeper engagement with AWS Lambda, focusing on enhancing efficiency and expanding functionality through integration and optimization.

⭐
LEVEL 4

Advanced

Advanced practitioners design event-driven architectures and implement robust error handling in AWS Lambda functions. They secure functions using VPC and IAM policies and utilize Lambda Layers for code reuse. This level requires a strategic approach to building scalable, secure, and efficient serverless applications, emphasizing architecture design and security best practices.

🏆
LEVEL 5

Expert

Experts architect complex serverless applications, implement CI/CD pipelines, and troubleshoot performance issues in AWS Lambda. They leverage Lambda for real-time data processing and analytics, demonstrating mastery in deploying and managing large-scale serverless solutions. This level reflects a comprehensive understanding of AWS Lambda's capabilities, focusing on innovation and optimization in complex environments.

Micro Skills

✎
LEVEL 1

Fundamental Awareness

Defining serverless computing and its characteristics
Explaining the benefits of serverless architecture
Comparing serverless computing with traditional server-based models
Identifying common serverless platforms and providers
Listing scenarios where AWS Lambda is beneficial
Exploring real-world examples of AWS Lambda applications
Understanding limitations and constraints of AWS Lambda
Evaluating AWS Lambda suitability for different workloads
Logging into the AWS Management Console
Locating the AWS Lambda service in the console
Understanding the layout and features of the AWS Lambda dashboard
Accessing AWS Lambda documentation and resources from the console
Explaining the AWS Lambda pricing structure
Calculating costs based on execution time and requests
Understanding the free tier limits for AWS Lambda
Identifying factors that influence AWS Lambda costs
🌱
LEVEL 2

Novice

Accessing the AWS Management Console
Navigating to the AWS Lambda service
Clicking on 'Create function' to start the process
Choosing the 'Author from scratch' option
Entering a function name and selecting a runtime
Configuring basic settings such as memory and timeout
Reviewing and creating the function
Understanding the role of IAM in AWS Lambda
Creating a new IAM role with basic Lambda execution permissions
Attaching the IAM role to the Lambda function
Verifying the permissions associated with the IAM role
Testing the function to ensure it has the necessary permissions
Accessing the AWS Lambda function dashboard
Using the 'Test' feature in the AWS Lambda console
Creating a test event with sample input data
Executing the test event to trigger the function
Reviewing the execution results and logs
Understanding the integration between AWS Lambda and CloudWatch
Accessing CloudWatch logs for a specific Lambda function
Interpreting log data to analyze function execution
Setting up CloudWatch metrics to monitor performance
Creating CloudWatch alarms for specific function metrics
🌍
LEVEL 3

Intermediate

Setting up S3 event notifications to trigger AWS Lambda functions
Configuring DynamoDB Streams to invoke AWS Lambda functions
Using AWS SDKs to interact with S3 and DynamoDB within a Lambda function
Handling input and output data formats when integrating with S3 and DynamoDB
Defining environment variables in the AWS Lambda console
Accessing environment variables within the Lambda function code
Securing sensitive information using encrypted environment variables
Updating environment variables without redeploying the Lambda function
Creating new versions of an AWS Lambda function
Understanding the use of $LATEST version in AWS Lambda
Creating and managing aliases for different Lambda function versions
Routing traffic between different versions using aliases
Adjusting memory allocation to optimize performance
Reducing cold start latency in AWS Lambda functions
Implementing efficient logging practices to minimize costs
Analyzing AWS Lambda execution metrics to identify optimization opportunities
⭐
LEVEL 4

Advanced

Identifying suitable events for triggering AWS Lambda functions
Configuring AWS Lambda triggers from services like S3, SNS, and DynamoDB Streams
Implementing asynchronous and synchronous invocation patterns
Utilizing AWS Step Functions for orchestrating complex workflows
Configuring AWS Lambda function error handling with try-catch blocks
Setting up AWS Lambda Dead Letter Queues (DLQ) for failed executions
Implementing custom retry logic using AWS SDKs
Monitoring and logging errors using AWS CloudWatch Logs
Configuring AWS Lambda to access resources within a VPC
Creating and attaching IAM roles with least privilege permissions
Implementing resource-based policies for AWS Lambda
Using AWS Secrets Manager to manage sensitive information
Creating AWS Lambda Layers for shared libraries and dependencies
Managing versioning of AWS Lambda Layers
Configuring AWS Lambda functions to use multiple layers
Optimizing AWS Lambda Layer size for performance
🏆
LEVEL 5

Expert

Identifying microservice boundaries
Defining communication patterns between microservices
Ensuring scalability and resilience
Configuring API Gateway endpoints
Securing API Gateway with IAM and Cognito
Monitoring and logging API Gateway requests
Defining state machines in AWS Step Functions
Integrating Step Functions with AWS Lambda
Monitoring and debugging Step Functions executions
Setting up Amazon Kinesis streams
Processing Kinesis stream data with AWS Lambda
Monitoring and optimizing Kinesis data pipelines
Creating a CodePipeline pipeline
Deploying AWS Lambda with CodePipeline
Monitoring and troubleshooting CodePipeline executions
Setting up build environments in CodeBuild
Building and packaging Lambda functions
Optimizing build performance and cost
Configuring deployment groups in CodeDeploy
Deploying Lambda functions with CodeDeploy
Monitoring and optimizing CodeDeploy deployments
Setting up Jenkins for AWS Lambda deployments
Automating AWS Lambda deployments with Jenkins
Monitoring and maintaining Jenkins pipelines
Enabling AWS X-Ray for Lambda functions
Analyzing X-Ray traces and segments
Optimizing application performance with X-Ray
Setting up CloudWatch Logs for AWS Lambda
Analyzing CloudWatch Metrics for Lambda performance
Optimizing Lambda performance with CloudWatch
Understanding the causes of cold starts
Mitigating cold start latency
Monitoring and measuring cold start occurrences
Determining optimal memory allocation
Configuring appropriate timeout settings
Monitoring and adjusting resource settings
Configuring Kinesis data streams for Lambda
Implementing Lambda functions for stream processing
Monitoring and scaling stream processing applications
Designing data transformation logic
Integrating Lambda with data sources and sinks
Monitoring and optimizing data transformation pipelines
Setting up data ingestion pipelines
Querying and analyzing data in Redshift
Optimizing Redshift performance for analytics
Setting up Elasticsearch clusters for log storage
Implementing Lambda functions for log processing
Monitoring and analyzing log data in Elasticsearch

Skill Overview

  • Expert2 years experience
  • Micro-skills118
  • Roles requiring skill0

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