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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  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
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Here's Python Program for List Duplicates

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Here is a program to find the item that occurs most frequently in a data structure. So why to find frequent item? Maybe it is the most purchased item on your shopping site. Perhaps it is the web page that gets hit the most often. If you are a tester, it could easily be the test that has had the most failures over the last year. Whatever it is, you want an easy way to find the data you need, and Python is here to help you. Python List duplicates Here are the two simple lists: list_1 = [1,2,3,2,3,2]  list_2 = ['a', 'b', 'a', 'b', 'c'] We can't do simple math on the individual items  since the second list contains characters. For example, it could contain the words of a book, and you want to find the most commonly used word in the work.  Also, it maybe list of UPC values for commonly purchased items. Whatever it is, all we can guarantee is that the data is probably comparable, in that we can compare one of the items to another. Yet we need to f
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These Tips Helpful to Remove Python List and Dictionary Duplicates

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In this post, I have shared top ideas to remove duplicates from the list. Those are with Append and Dictionary. 1. How to Remove Duplicates with Append # Here is a list with duplicates list_with_duplicates = [1,2,3,12,1,2,3,4,5,6,1,2,3,7,8,9] It is simple if you follow the first-approach - brute force approach: list_without_duplicates = [] for pd in list_with_duplicates:   if pd not in list_without_duplicates:       list_without_duplicates.append(pd) print(list_without_duplicates) Result: [1, 2, 3, 12, 4, 5, 6, 7, 8, 9] This method has performance issues when the list is bigger in size.  Real-time. 2. How to Remove Duplicates with Dictionary # Here is you can convert a list to a dictionary dict_without_duplicates = dict(zip(list_with_duplicates, list_with_duplicates)) print(dictionary_without_duplicates) Result: {1: 1, 2: 2, 3: 3, 12: 12, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9} Real-time. Once again, this works and has the advantage of taking less space than duplicating the entire list. 
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