Build and Deploy Lambda Functions: AWS with Python and Boto3

Prepare your development environment by installing Python and boto3, configuring AWS access keys, and exploring the AWS console. Follow platform-specific setup videos to install tools and set your region.

Navigate the tools and environment for AWS development with Python and boto3, configure credentials, and set up PyCharm and environments for macOS, Windows, and Linux.

Create an AWS account by signing up through aws.amazon.com, review the free tier benefits, and complete identity verification with credit card and phone call.

Sign into the AWS management console, switch regions, and access account, billing, and recent services through the navigation. Explore service discovery, pinning favorites, and overview of costs, organizations, and governance.

Create an IAM user for programmatic access, attach policies, enable multi-factor authentication, and generate the access key and secret key for AWS resources.

Install Python 3 and pip on Windows by downloading from python.org and customizing the installer. Add Python to PATH and verify by opening a command prompt and typing Python.

Install and verify the AWS CLI on Windows, downloading the 64-bit installer, accepting license terms, adjusting the path and environment variables, and confirming the aws command runs.

Configure the AWS CLI with access key and secret key to enable Boto3 and Python to access AWS resources, specify the Frankfurt eu-central-1 region, and set a default output format.

Install boto3 and its dependencies with pip to prepare your Python environment for working with AWS resources.

Verify AWS credentials with AWS configure to ensure access key, secret key, and region are set. Then confirm Python and boto3 are installed using pip show boto3.

Install python 3 and the python package manager pip on macOS, then verify the installations in the terminal by running python3 and pip3.6 to access python 3.6 and manage packages.

Install and configure the AWS CLI on macOS using Homebrew, enabling access to AWS resources from the command line by configuring access keys and secret keys.

Configure AWS credentials with the AWS configure command, entering the access key, secret key, and default region, then install boto3 for Python to access the AWS API.

Install boto3 using pip to enable Python projects to interact with AWS APIs, setting up for building and deploying lambda functions.

Verify python 3.6 installation, confirm aws cli is configured with access keys and region, and ensure boto3 is installed so you can start building lambda functions with aws resources.

Set up a new Python project in PyCharm IDE, create a Python package named SIRC, and configure an AWS Lambda client with boto3 in region us-west-1.

Create an identity and access management client, define a policy for lambda to access s3 and cloudwatch logs, serialize it as json, and attach it to the lambda execution role.

Create an IAM execution role for a Lambda function to permit actions against AWS services, define the assume role policy, and attach the access policy to enable execution.

Attach the IAM access policy to the Lambda execution role using the IAM client attach_role_policy, confirming the policy is linked and ready to proceed to function creation.

Deploy a lambda function by building a generic deployment method that accepts function name, runtime, handler, role, and source folder, reads the code, and creates a zip for AWS.

Create a Python utils class with static methods to build a zip file from a source directory and read its bytes for AWS integration, using os.walk and zipfile.

Develop a Python lambda function by creating a handler, wiring event and context, and returning a JSON response with a 200 status and a hello from Python lambda function message.

Deploy and configure a Python lambda function using boto3, including code, zip payload, runtime, handler, role, timeout, and memory, then deploy and verify in AWS.

Navigate the AWS console to review a Python 3.6 lambda function's configuration, including handler, timeout, memory, and execution role, with CloudWatch logs and S3 permissions, and learn to invoke it.

Develop a Node.js lambda function by creating a JavaScript file, exporting a handler, and using a callback to return a 200 status and a message, then deploy the function.

Deploy a nodejs lambda function by configuring the runtime, naming the function, and invoking a generic deploy method to create the AWS lambda and view its response.

Explore the AWS console to inspect a Node.js lambda function, verify its execution role policies for S3 and CloudWatch logs, and review its source code, runtime, handler, memory, and timeout.

Learn how to install the AWS toolkit plugin in Eclipse to create and deploy Java lambda functions, using the Eclipse marketplace and integrating with AWS services.

Develop and deploy a Java AWS lambda function using the Eclipse AWS plugin, Maven packaging, and a jar deploy artifact, with testing and handling requests.

Deploy a Java lambda function by configuring a Java 8 runtime, packaging a jar, and uploading its bytes to AWS Lambda, including setting the correct handler and package path.

Deploy and verify a Java 8 AWS lambda function in the console, ensuring proper execution role, policies, CloudWatch logging, a complete code package, and readiness for versions and aliases.

Invoke lambda functions on aws by passing the function name to a lambda client and decode the payload to read the 200 status and the hello from Python lambda function.

Update your lambda configuration to add environment variables as key-value pairs, then read and use them in your python function to process data and interact with aws resources.

Learn to read environment variables in a Python lambda using the os library and apply them in function responses, then update and invoke the function to verify.

Publish a lambda function to make it visible to other AWOS services, moving it from unpublished to published, and learn to define versions and aliases, including latest and version numbers.

Create aliases for AWS Lambda function versions to tag environments like production, staging, and development. Invoke the function by its alias and use Python to map aliases to specific versions.

Invoke a production version of an AWS Lambda function using its alias as a qualifier. Decode and print the payload, then leverage aliases and versioning to manage changes.

Learn to fetch a lambda function's configuration using a get_function method in Python with boto3, seeing details like runtime, function name, handler, role, and last modified date.

List all lambda function configurations by calling list functions and retrieving every function's settings, including Java and Python functions, across your AWS account.

Update the configuration of an existing AWS Lambda function by increasing its memory size with update function configuration, specifying the function name and the new memory value.

Learn to delete AWS lambda functions by calling the delete function with the target function name and verifying the result in the AWS console.

Discover how AWS Lambda serves as the serverless backbone by packaging and deploying code in Python or other languages, without managing infrastructure.