Python Programming for AWS - Learn Python with AWS and Boto3

Explore AWS fundamentals by identifying core services and creating a user account with permissions. Get hands-on with VPC, EC2, and S3 in the console, then apply Python later.

Master the AWS identity and access management overview, defining principals, users, roles, and policies. Learn how identity-based and resource-based policies authorize or deny API actions like RunInstances and GetBucket.

Create an IAM user to sign in to the AWS console, assign permissions via a group with the administrator policy, and avoid using the root account.

Explore how EC2 provides virtual servers in the cloud with configurable instance types, operating systems, and AMIs built from EBS snapshots, offering elastic computing and pay-for-what-you-use cost efficiency.

Launch Linux and Windows EC2 instances in a public subnet within a VPC, configure EBS storage, security groups, and key pairs for secure internet access.

Learn to connect to EC2 instances using instance connect and SSH, including browser access with a temporary key pair, port 22 open in the security group, and IAM-based security controls.

Learn to access a Windows EC2 instance via RDP by configuring security groups for port 3389, using the administrator account, and decrypting the password with your private key.

Compare access keys with instance profiles to enable an EC2 instance to access storage and AWS services securely, without storing credentials on the instance via IAM roles.

Demonstrates configuring access keys and IAM roles to grant an EC2 instance S3 access, highlighting credentials risk and the safer approach of using an EC2 role.

Explore Amazon VPC fundamentals across regions and availability zones, including subnets, route tables, internet gateways, CIDR blocks, and how security groups and network ACLs control traffic.

Create a custom VPC with a 10.0.0.0/16 CIDR, two public and two private subnets across two AZs, an internet gateway, and main and private route tables.

Learn how Amazon S3 uses buckets and objects to store files, accessed via REST API over HTTP, with unique global bucket names, region scoping, and strong read-after-write consistency.

Explore creating s3 buckets and uploading objects in the management console, with region, versioning, and encryption options. Use bucket policies and AWS CLI commands to manage access and copy files.

Master Python fundamentals and core concepts, from the print function and variables to data types, operators, conditionals, loops, lists, dictionaries, and functions. Use debugging to troubleshoot and refine your code.

Understand Python as a high-level language with easy syntax, built-in data structures, and modularity for code reuse, plus real-world AWS use cases like emailing and uploading files to buckets.

Learn to comment Python code with purposeful explanations that reveal a function’s intent, clarify condition checks, and describe variables, helping future you and other developers.

Explore how the Python print function displays strings and numbers, concatenates values, converts integers to strings, and inserts newlines, using Cloud9 IDE to run scripts for validation and debugging.

Explore variables as boxes that hold integers, strings, and data structures, enabling data transfer between functions and clearer, maintainable code, and learn to embed values with Python 3 f-strings.

Explore four basic Python data types: integers, floating point numbers, strings, and booleans—through variable declarations, printing, and examples using quotes.

Explore Python operators, including assignment, comparison, membership, arithmetic, and logical operators, with practical examples like calculating subtotals, discounts, and free shipping eligibility.

Learn how Python conditional statements drive decision making by mapping user requirements to AWS services (S3, EC2, Lambda) with if/elif logic and a final unknown fallback.

Learn how Python lists work as ordered containers, access elements by zero-based indices, modify and append items, and slice lists using built-in methods, with AWS services as examples.

Explore Python dictionaries through key-value pairs, accessing items by keys, and nesting, with hands-on AWS examples using S3, Lambda, and EC2 descriptions.

Explore Python loops, including for and while constructs, and how they handle definite versus indefinite iteration. See the enumerate function in action with an AWS services list.

Explore functions in Python by building a shoe factory example: use built-in and user-defined functions to implement modular, reusable code that returns a shoe type based on a materials list.

Create a create_shoe function that examines a materials list (leather, rubber, mesh) and returns a shoe dictionary with type and materials; warn on unknown types.

Explain python exception handling with built-in errors like file not found and key error, illustrate custom exceptions, and use try/except to maintain flow in aws s3 log scenarios.

Learn Python exception handling by using a try/except block to gracefully manage key errors when querying an AWS services statuses dictionary, returning none or a friendly message.

Learn practical python debugging techniques, including inspecting code, using print statements and logs, and leveraging IDE breakpoints to step through code and fix errors in cloud or local environments.

Consolidate Python fundamentals by strengthening functions, lists, dictionaries, loops, and debugging. Explore documentation and pep8, then transition to AWS services using Python with the Boto3 SDK.

Explore AWS services using Python to interact with services via boto3, learning simple APIs for S3, EC2, RDS, and VPC operations, gaining practical insights into data manipulation and cloud control.

Explore how the AWS Boto3 SDK enables Python developers to automate AWS services, using resources for Pythonic access and clients for broader control, from S3 uploads to EC2 fleet creation.

Explore the boto3 documentation to identify the right method for your AWS tasks using Python. Compare clients and resources and review examples for uploading to S3 and creating EC2 fleets.

Configure boto3 in your AWS cloud nine environment by creating a project directory, installing boto3 with pip, and listing S3 buckets with a boto3 S3 resource.

Explore creating, reading, updating, and deleting S3 objects with the boto3 SDK, using resource methods to manage buckets and objects with proper permissions.

Perform full CRUD operations on an S3 resource using Python and boto3, creating a bucket, uploading and reading files, updating contents, and cleaning up by deleting objects and the bucket.

Learn EC2 basics and how to launch and manage EC2 instances with Python using boto3, including resources vs. clients, and actions like start, stop, and terminate.

Learn to launch and manage an EC2 instance with Boto3 in Python on Cloud9, including creating, checking existence, starting, stopping, and terminating the DCT EC2 HOL instance.

Explore VPC basics, creation, and management in AWS using Boto3, creating VPCs with subnets, route tables, internet gateways, and tags via EC2 clients and resources.

Create a vpc with boto3 in python using cidr 10.0.0.0/16, set a name, tag it, and print the id. Then check for existing vpcs and attach an internet gateway.

Create a VPC with a public route to the internet, using EC2 and Cloud9, and configure three subnets in us-east-1a, 1b, and 1c with a route table and internet gateway.

Explore RDS basics and Aurora serverless options, learn to manage and configure databases with Python using boto3, including creating, scaling, and securing DB clusters.

Create an Aurora serverless RDS cluster in Cloud9 using boto3 by configuring a DB subnet group and VPC, enabling the HTTP endpoint, and monitoring for availability.

Modify an Aurora serverless RDS cluster’s scaling from 1 to 16, set auto pause to 10 minutes using boto3, verify changes, then delete the cluster with skip final snapshot.

Deepen your Python skills with AWS by using Boto3 to manage S3, EC2, VPC, and RDS, modify scripts to create Aurora tables, and prepare for Lambda automation.

Explore how lambda functions automate aws tasks and streamline repetitive work without provisioning servers. Learn to integrate lambda with ec2, s3, rds, and vpc through hands-on real-world scenarios.

Learn how AWS Lambda provides a serverless Python runtime that scales with events and pays for compute time and requests, enabling file processing in S3 and image resizing with Pillow.

Update your AWS Cloud9 environment from Python 3.7 to 3.9, install Python 3.9.5, configure aliases, and reinstall boto3 to ensure compatibility with Lambda functions.

Create a default lambda function in the lambda console, download its boilerplate Python code to Cloud9, edit locally, and upload changes back to lambda as the latest version.

Automate EC2 tasks using Lambda to schedule, stop, snapshot, and auto scaling instances, reducing errors and costs. Learn to trigger Lambda from EC2 events and use EventBridge for backups.

Code and deploy a Lambda function that uses boto3 to create an EC2 snapshot at regular intervals via EventBridge, with Cloud9 testing and required IAM permissions.

Create a recurring event bridge schedule to trigger the EC2 daily snapshots Lambda function. Configure a 24-hour rate-based schedule and review CloudWatch logs for debugging.

Automate S3 with Lambda to trigger file processing on upload, enable alerts, and clean up via event-driven Python functions for S3 object created and removed events, including post, copy, delete.

Create a lambda that validates csv files on s3 uploads, parse each row, flag discrepancies, and move failed files to the error bucket, with local testing in Cloud9.

Parse the CSV with a Lambda function to extract date, product line, currency, and bill amount, validate the date format, and move erroneous files to the billing errors bucket.

Automate real-time data validation by deploying a billing bucket parser lambda to S3, configuring a put event trigger and permissions, and moving erroneous CSV files to the error bucket.

Automate mundane database tasks with lambda and rds to enable scalable, event-driven operations, data transformation, and microservice-driven architectures using serverless etl patterns.

Configure a lambda function triggered by s3 uploads to convert non-usd billing amounts to usd and insert the normalized records into an aurora serverless v1 database, with secrets manager integration.

learn to automate rds data ingestion with lambda by reading csv files from s3, normalizing currencies to usd, and inserting transformed billing records into an aurora rds cluster.

Automate data ingestion from a billing data bucket to Aurora Serverless v1 with a Lambda function triggered by S3, transforming currencies to USD and managing access via Secrets Manager.

Automate VPC operations with AWS Lambda to enable event driven networking, improve security, and automate common tasks. Manage Elastic IP addresses automatically to save costs and adapt to demand.

Automate elastic IP management with a Python Lambda using Boto3 to release unassociated EIPs and manage EC2 associations via an event bridge trigger.

Automate VPC management by releasing unassociated elastic IPs with a Lambda function, configure EventBridge triggers, and set EC2 permissions to run daily.

Merge hands-on AWS Lambda and Python lessons to automate S3 billing file parsing, move errors to an error bucket, and upload clean data to an RDS cluster.

Master Aws services with python, from Amazon simple notification service, simple queue service, and simple email service for messaging, to glue and elastic mapreduce for data, and automating aws security.

Use sns, sqs, and ses with python to integrate notifications, queuing, and emailing in an ecommerce workflow, publishing to topics, processing orders, and sending customer emails.

Automate end-to-end CSV validation and error handling with SNS and SQS by orchestrating S3 bucket events, a Lambda-based billing parser, and a retry workflow for failed third-party API calls.

Test the retry billing parser lambda locally on cloud nine, simulate an unsuccessful API call with SNS email alerts, and configure SQS triggers, permissions, and bucket routing for automated processing.

Learn how AWS Glue automates data discovery and cataloging with crawlers and lake formation, and how EMR with PySpark transforms data and writes results to S3.

Catalog csv data from S3 using AWS Glue crawlers, run a Spark script on an EMR cluster to compute gross profit per product, and store the report in data lake.

Execute a hands-on lab with AWS Glue and EMR to catalog billing data, run PySpark scripts, compute gross profit, and store the results in a CSV on S3.

Learn to automate Aws security with python and boto3 across Iam, Config, and Security Hub, automating compliance checks, findings from GuardDuty, Inspector, and Macie, and real-time responses via Lambda.

Develop an automated AWS security auditing system that scans security groups for 0.0.0.0/0 inbound rules, compiles a findings report, and emails it via a Python Lambda, SNS, and EventBridge.

Advance your AWS and Python skills by exploring AWS Glue, EMR, SNS, SQS, SES, building security automation with Security Hub and Lambda to revoke IAM credentials after unauthorized access.

Explore tools for python and aws development to boost productivity, including aws code as a managed source control service on aws, and ai aids like ChatGPT and GitHub Copilot.

Master version control with AWS CodeCommit by creating repositories, pushing code from Cloud9, and collaborating through branches and merges.

Learn to use ChatGPT as a Python development tool for the cloud, generating boilerplate code and debugging boto3 scripts for EC2, while validating outputs to avoid hallucinations.

Explore how GitHub Copilot acts as a virtual pair programmer to generate a local Python script that processes CSV files and uploads them to S3.

Explore how to programmatically interact with AWS services using tools to aid software development and automate tasks with Python, building hands-on projects.

Celebrate completing this section by integrating Copilot, AWS CodeCommit, and ChatGPT into your AWS and Python workflow to speed development.