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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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The best helpful HDFS File System Commands (2 of 4)

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#Top-Selected-HDFS-file-system-commands CopyFrom Local Works similarly to the put command, except that the source is restricted to a local file reference. hdfs dfs -copyFromLocal URI hdfs dfs -copyFromLocal input/docs/data2.txt hdfs://localhost/user/rosemary/data2.txt HDFS Commands Part-1of 4 copyToLocal Works similarly to the get command, except that the destination is restricted to a local file reference. hdfs dfs -copyToLocal [-ignorecrc] [-crc] URI hdfs dfs -copyToLocal data2.txt data2.copy.txt count Counts the number of directories, files, and bytes under the paths that match the specified file pattern. hdfs dfs -count [-q] hdfs dfs -count hdfs://nn1.example.com/file1 hdfs://nn2.example.com/file2 cp Copies one or more files from a specified source to a specified destination. If you specify multiple sources, the specified destination must be a directory. hdfs dfs -cp URI [URI …] hdfs dfs -cp /user/hadoop/file1 /user/hadoop/file2 /user/hadoop/dir du Disp
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