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8 Ways to Optimize AWS Glue Jobs in a Nutshell

  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif

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())



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.")


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())


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)


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)


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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