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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

How to Find Non-word Character: Python Regex Example

In Python, the regular expression pattern \W matches any non-word character. Here's an example of usage. The valid word characters are [a-zA-Z0-9_]. \W (upper case W) matches any non-word character.

regex non-word character

Regex examples to find non-word char

#1 Example

import re
text = "Hello, world! How are you today?"
non_words = re.findall(r'\W', text)

In the above example, the re.findall() function is used to find all non-word characters in the text string using the regular expression pattern \W. The output will be a list of non-word characters found in the string:


[',', '!', ' ', ' ', '?']

This includes punctuation marks and spaces but excludes letters, digits, and underscores, which are considered word characters in regular expressions.

#2 Example

import re
text = "Hello, world! How are non-word-char:! you today?"
non_words = re.findall(r'non-word-char:\W', text)



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