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RDS PostgreSQL and DynamoDB CRUD: AWS with Python and Boto3

Implement RDS PostgreSQL CRUD and DynamoDB on AWS using Python API - Boto3 and psycopg2! Build infrastructure with code!
Instructor:
Niyazi Erdogan
330 students enrolled
English [Auto-generated]
You'll be able to implement any sort of infrastructure on AWS with Python using RDS and DynamoDB!
You'll learn how to code against AWS API using Python and Boto3!
You'll learn how to launch and configure Relational Database Instances (RDS) on AWS using Python and Boto3!
You'll learn how to connect to RDS PostgreSQL instances on AWS using Python and psycopg2!
You'll learn how to implement Create, Read, Update and Delete (CRUD) operations on RDS PostgreSQL using Python and psycopg2 library!
You'll learn how to create and configure NoSQL DynamoDB Tables on AWS using Python and Boto3
You'll learn how to implement Create, Read, Update and Delete (CRUD) operations on DynamoDB using Python and Boto3!
You'll be confident to work with AWS APIs using Python for any kind of AWS resource on RDS and DynamoDB!

Do you want to learn how to launch managed Relational Databases or RDS on AWS? Do you want to learn how to connect to your RDS DB instances using Python and psycopg2 library and implement all Create, Read, Update and Delete (CRUD) operations? Or do you want to learn how to implement NoSQL DynamoDB Tables on AWS and work with data from scanning, querying to update, read and delete operations?

Then this is the course you need on RDS and DynamoDB on AWS!

In this course, we’ll start by taking a look at the tools and the environment that we need to work with AWS resources. We’ll be using Python 3 and as per the IDE I recommend you to use PyCharm from Jetbrains. It has a free community edition even!

After I teach you how you can set up your environment on both MacOS and Windows, we’ll create our credentials for AWS as being the AWS Access Key and AWS Secret Access Key for programmatic access to AWS resources. You’ll learn how you can set your AWS credentials globally on your computers using AWS CLI. Before jumping into the implementation, for one last tip, I’ll show you how you can have auto-complete capabilities on your PyCharm IDE with PyBoto3!

Once we’re ready with our environment setup, we’ll start implementing our solution on AWS! And remember we’ll do everything with Python code; not a single thing manually or by hand!

We’ll start off with RDS or Relational Database Service from AWS. I’ll teach you how to launch your own Amazon RDS Instances purely with your Python code! Then we’ll learn how to connect to our RDS database instance using Python and psycopg2 library. After that, I’ll teach you how to execute your queries against RDS PostgreSQL using psycopg2 library and we’ll implement SELECT, INSERT, DELETE, UPDATE so basically all the CRUD opreations against our own-launched RDS PostgreSQL instance on AWS!

Next up is DynamoDB! With this very-popular NoSQL service from AWS, I’ll teach you how to create your own DynamoDB Tables on AWS with Python! You’ll learn how to provide a key schema, attribute definitions and apply throughput to your tables.

And I’ll share the great news for you that there is a Local version of DynamoDB that you can simply run on your computer to play around with! I will show you how you can get and run the Local version of DynamoDB on your computer and we’ll setup our environment and boto3 client configuration accordingly.

Then we’ll start making our way to putting new items, updating, deleting and reading them. Once we learn the basic CRUD operations with DynamoDB, we’ll move on to rather advanced operations like scanning and querying.

We’ll also implement a script to insert our sample data set of “movies” into our DynamoDB Movies table! Once we insert the data, we’ll start exploring how we can search it using DynamoDB query operation and we’ll also learn how we can use conditions. And finally, we’ll take a look at the scan operation which basically scans your whole data and retriveves the results you need. So to filter out the results from scan operation, we’ll apply filter expressions to our scan operation and see how things work with DynamoDB.

Lots of information, hands-on practice and experience is waiting for you in this course on AWS. So, don’t miss any more time and join me in this course to sharpen your skills on AWS using Python and Boto3!

Introduction

1
Welcome
2
What We Will Build in This Course
3
About This Course
4
What Do You Need For This Course

Preparing The Environment for Development

1
Section Overview
2
About Tools and Environment
3
Create an AWS Account If You Don't Already Have It!
4
Logging in to AWS Console and A Walkthrough
5
Let's Create Our AWS Credentials!

Windows Environment Setup

1
Installing Python 3 and Pip
2
Installing AWS SDK (CLI)
3
Preparing CLI with AWS Credentials
4
Installing Boto3
5
Verifying The Setup

MacOS Environment Setup

1
Installing Python 3 and Pip
2
Installing AWS SDK (CLI)
3
Preparing CLI with AWS Credentials
4
Installing Boto3
5
Verifying The Setup

Relational Database Service (RDS) with Boto3: psycopg2

1
Section Overview
2
Setting Up Our Project and Preparing and Launching RDS Instance
3
AWS Console Checkpoint: RDS Instance
4
Security Group and DB Subnet for RDS
5
Verifying Connection with Postico
6
Building Configuration File For RDS Instance Credentials
7
Connecting to RDS Instance with psycopg2
8
Creating Table
9
Inserting Data
10
Reading Data
11
Updating Data
12
Deleting Data
13
Section Summary

DynamoDB with Boto3

1
Section Overview
2
Setting Up Our Project with PyCharm IDE
3
Good News - Downloadable Version of DynamoDB!
4
Preparing DynamoDB in Local Environment
5
Creating Table
6
Writing Data: Putting Items
7
Writing Data: Updating Items
8
Writing Data: Conditionally Updating Items
9
Reading Data: Getting Items
10
Writing Data: Conditionally and Unconditionally Deleting Items
11
Reading Data: Preparing Our Sample Data
12
Reading Data: Querying Items
13
Reading Data: Conditionally Querying Items
14
Reading Data: Scanning Items
15
Section Summary

Wrapping Up

1
What Have We Learned?
2
What Comes Next?
3
Thank You!
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