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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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2 Tricky Examples Python List Comprehension

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Here are the tricky examples of list comprehension. These examples useful for your project and interviews as well. 1. Printing odd numbers Reminder checking logic used here. If it is not equal to zero when you divide the number with 2, it treats the input number as odd and prints it. lst1 = [x for x in range(40) if x % 2 != 0] print('First 20 Odd Numbers:') print(lst1) 2. Printing even numbers Reminder checking logic used here. If it is equal to zero when you divide the number with 2, it treats the input number as even and prints it. lst2 = [x for x in range(40) if x % 2 == 0] print('First 20 Even Numbers:') print(lst2) Output from the Python scripts First 20 Odd Numbers:  [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39] First 20 Even Numbers:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]  ** Process exited - Return Code: 0 ** Press Enter to exit terminal Related The real use of Git in Dev Ops
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