![]() ![]() 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! 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. We’ll start off with RDS or Relational Database Service from AWS. 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! Before jumping into the implementation, for one last tip, I’ll show you how you can have auto-complete capabilities on your P圜harm IDE with PyBoto3! You’ll learn how you can set your AWS credentials globally on your computers using AWS CLI. It has a free community edition even!Īfter 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. We’ll be using Python 3 and as per the IDE I recommend you to use P圜harm from Jetbrains. In this course, we’ll start by taking a look at the tools and the environment that we need to work with AWS resources. Then this is the course you need on RDS and DynamoDB on AWS! I’m uploading two files, and running through all the code blocks to connect to my DB instance and work with it.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? Video Walkthroughīelow is a screen recording of me going through the Colab process. In this case, I’ve downloaded the iris dataset and I will upload it to my DB instance as the table iris. In the Google Colab environment, you need to upload two files: your credentials json file, and your dataset. ![]() Here is the link to the Python Notebook that you can upload to your own Colab environment. I’m going to share an example Colab Notebook with you so you should be up and running fast. Most of my ad-hoc work is done in Google Colab because it’s easy to run code blocks and debug interactively.
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