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Step-by-Step Guide to Creating an AWS RDS Database Instance

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 Amazon Relational Database Service (AWS RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Instead of managing servers, patching OS, and handling backups manually, AWS RDS takes care of the heavy lifting so you can focus on building applications and data pipelines. In this blog, we’ll walk through how to create an AWS RDS instance , key configuration choices, and best practices you should follow in real-world projects. What is AWS RDS? AWS RDS is a managed database service that supports popular relational engines such as: Amazon Aurora (MySQL / PostgreSQL compatible) MySQL PostgreSQL MariaDB Oracle SQL Server With RDS, AWS manages: Database provisioning Automated backups Software patching High availability (Multi-AZ) Monitoring and scaling Prerequisites Before creating an RDS instance, make sure you have: An active AWS account Proper IAM permissions (RDS, EC2, VPC) A basic understanding of: ...

Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations:


Regular expression examples


01 Matching a Simple Pattern


import re


text = "Hello, World!"

pattern = r"Hello"

result = re.search(pattern, text)

if result:

    print("Pattern found:", result.group())

Output:


Output:

Pattern found: Hello

This example searches for the pattern "Hello" in the text and prints it when found.


02 Matching Multiple Patterns


import re


text = "The quick brown fox jumps over the lazy dog."

patterns = [r"fox", r"dog"]

for pattern in patterns:

    if re.search(pattern, text):

        print(f"Pattern '{pattern}' found.")

Output:


Pattern 'fox' found.

Pattern 'dog' found.

It searches for both "fox" and "dog" patterns in the text and prints when they are found.


03 Matching Any Digit

 

import re


text = "The price of the product is $99.99."

pattern = r"\d+"

result = re.search(pattern, text)

if result:

    print("Price:", result.group())

Output:


Price: 99

This example extracts digits (numbers) from the text.


04 Matching Email Addresses


import re


text = "Contact us at support@example.com or info@example.org."

pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b"

emails = re.findall(pattern, text)

for email in emails:

    print("Email:", email)

Output:

Email: support@example.com

Email: info@example.org

It extracts email addresses from the text using a common email pattern.


05. Replacing Text

 

import re

text = "Please visit our website at http://www.example.com."

pattern = r"http://www\.[A-Za-z]+\.[A-Za-z]+"

replacement = "https://www.example.com"

updated_text = re.sub(pattern, replacement, text)

print("Updated Text:", updated_text)

Output:

Updated Text: Please visit our website at https://www.example.com.

This example replaces a URL with a different URL in the text.


These are just a few examples of what you can do with regular expressions in Python. Regex is a versatile tool for text processing, and you can create complex patterns to match specific text structures or extract information from text data.

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