RDS PostgreSQL and DynamoDB CRUD: AWS with Python and Boto3

Identify development tools and environment setup by configuring Python versions, Boto3, AWS CLI, access keys, secret keys, then create an AWS account, set the region, and explore the console.

Configure AWS credentials and region, then call AWS APIs with Python and boto3; use PyCharm for Python development across macOS, Windows, and Linux.

Create an AWS account to access the free tier and practice with EC2, S3, and DynamoDB by completing sign-up with email, contact info, credit card, and phone verification.

Sign in to the AWS management console, switch regions, and explore services, then review account details, billing, and cost explorer while learning about AWS organizations and pinning frequently used services.

Create an IAM user for programmatic access, attach policies, enable multi-factor authentication, and securely manage access keys and secret keys for AWS CLI automation.

install python 3.6.5 on windows from python.org and customize installation to include pip. add python to path and verify the setup by opening the command prompt and running python.

Install and verify the AWS CLI on a Windows machine by downloading the 64-bit installer, completing the setup, and updating environment PATH variables for system-wide use.

Configure the AWS CLI with your access key, secret key, and eu central one region, then set json output to access AWS resources via any language.

Install the boto3 library for Python using pip to start working with AWS resources, as pip downloads boto3 and its dependencies like core, s3 transfer, documentation, and utils.

Verify AWS credentials with AWS configure, confirm access key, secret key, and region; ensure Python 3.6.5 is installed and boto3 is ready (pip show boto3, version 1.7.2).

Install python 3 and the pip package manager on macOS by downloading Python 3.6.x from python.org, running the installer, and verifying Python 3 and pip from the terminal.

Install the AWS CLI on macOS using Homebrew to configure access keys and access AWS resources from the command line. Leverage Homebrew to avoid export issues and simplify path variables.

Configure the aws cli with your access key and secret key, set the default region to eu central 1, choose json output, and install boto3 to access the aws api.

Install the boto3 library with pip to connect your Python project to AWS APIs. Confirm pip3.6 installation and rapidly get ready to work with AWS services using boto3.

Verify your setup by confirming Python 3.6 is installed, AWS CLI is configured with access key and secret, and boto3 is installed to work with VPC and EC2.

Launch a PyCharm project, set up a security group and subnet group, launch a public RDS instance, verify in AWS console, then create tables and perform basic CRUD operations.

Set up a PyCharm IDE project, create a src package, and implement code to launch an RDS instance on AWS with a security group, inbound rule, and DB subnet group.

Create a db subnet group and a security group for rds postgresql using boto3 in eu west 3, assigning default vpc subnets and opening port 5432.

Launch an RDS PostgreSQL instance by creating a security group and a subnet group, then configure a publicly accessible PostgreSQL 9.6.6 database on port 5432 with gp2 storage.

Verify a PostgreSQL RDS instance in the AWS console, confirm its availability, and connect using the AWS endpoint rather than an IP address.

Verify a connection to your RDS instance using Postico by entering endpoint, port 5432, username postgres, and password, then connect publicly; you can also use PgAdmin or the command line.

Create a connection configuration file database.ini for a postgres rds instance, using a postgres section with host, database, user, and password to connect via psycopg2.

Create a Python script to connect to a PostgreSQL RDS instance by parsing database.ini with configparser, installing and using psycopg2, and executing a version query.

Create and run SQL to build users and accounts tables in a PostgreSQL RDS instance, using serial primary keys and not null columns, via a Python psycopg2 workflow.

Define an insert vendor list method to insert data into the users table using a sql insert with placeholders, then connect, execute, commit, and handle errors.

Read data from a PostgreSQL database by establishing a psycopg2 connection, executing a select query to fetch user id and username ordered by username, and handle errors while closing connection.

Update user records in a PostgreSQL database using psycopg2, crafting an SQL update statement, handling connections, and verifying changes through row counts and refreshed queries.

Delete a user from PostgreSQL with psycopg2 in Python by executing a delete from users where user_id, tracking rows affected, and committing, then verify the user is removed.

Set up the PyCharm project and a database.ini to connect to an RDS PostgreSQL instance, then use Psycopg2 to perform create, read, update, and delete operations in preparation for DynamoDB.

Explore DynamoDB locally with the downloadable version to avoid costs, create and populate a table with sample data, and learn to query, scan, put, and update items.

Open PyCharm, create a new project named DynamoDB, set up a src package, and implement get DynamoDB client and DynamoDB resource with boto3 in eu-west-3.

Discover how to use the downloadable local version of DynamoDB to write and test applications offline, reducing AWS costs, and prepare code for easy migration to production with minor changes.

Prepare DynamoDB locally by downloading DynamoDB Local, running it on port 8000 with Java runtime, and configure boto3 to connect via localhost:8000.

Create a local DynamoDB table named movies with year and title attributes, a hash and range key, and provisioned read and write capacity using a Python client.

Learn to put an item into a DynamoDB table with Python, building a movie record (year, title, info with plot and a decimal rating), and handle errors and responses.

Update an existing DynamoDB item by key (year and title) and update expression to set info.rating, info.plot, and info.actors; a 2015 movie updated to 3.5 and new actors.

Learn to conditionally update DynamoDB items using update expressions, remove a list element, and handle conditional check errors with boto3 in Python.

Read an item from a DynamoDB table using year 2015 and the big new movie as the key with get_item; handle errors and read the item from response.item.

Learn to delete items from a DynamoDB table using conditional and unconditional deletes with boto3, handle client errors, and verify results.

Prepare and insert a sample movie dataset into dynamodb using python and boto3, enabling bulk reads through query and scan, with json loading and put_item.

Query DynamoDB to fetch movies released in 1985 using a key condition expression, showing how to scan and query items and print the year and title.

Learn to perform conditional DynamoDB queries by filtering movies from 1992 with titles from A to L, using a projection expression to retrieve year, title, genres, and lead actor.

Learn to perform a DynamoDB scan of an entire table, apply a filter expression for years 1950 to 1959, and use a projection expression to return year, title, and rating.

Set up a PyCharm project, run a local dynamodb, and practice CRUD operations on a movies table using Python and boto3, including conditional updates, queries, and scans.

Set up AWS credentials with IAM, install Python tools, and configure Boto3 for API calls. Build RDS PostgreSQL and DynamoDB projects using Python, psycopg2, and DynamoDB local for CRUD operations.

Discover what comes next in the AWS Python and Boto3 series, with future courses on S3, Lambda, SQS, SNS, and Route 53, building on RDS and DynamoDB fundamentals.

Explore how to manipulate and manage data with AWS RDS and DynamoDB using Python and Boto3, and understand their role as AWS data frontier services.