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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

Python Matrix Vs COBOL Arrays Top Differences

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Your most looking information where Python matrix and COBOL arrays differ, in this post, I am giving complete information. The Logic is different in both the languages. The way of definition and accessing element in an array or matrix is different. Python Matrix Vs COBOL Array. In reality both Array and Matrix are the same What are Arrays  Arrays are storing data structure to store data in one or more dimensional form. You can access the data for further processing in your application program. One Dimensional Array  In general, one dimensional array is a row of elements either numeric or Strings separated by commas. Here, each element is separated by comma. This is key concept. >>> a = ['Srini',25,33,42] Two Dimensional Arrays  In the case of Two dimensional array data stored in Tabular form and you can access whichever tuple you want. Real use of multi dimensional array is to give input in Tabular form and can access particular tuple as you want. >>>