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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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Data mining Real life Examples

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Data mining is a process to understand about unused data and to get insights from the data. You need a quick tutorial and examples to perfect with this process. The best example is the Backup data business use case to mine the data for useful information. The backup data is simply wasted unless a restore is required. It should be leveraged for other, more important things. This method is called Data Mining Technique . --- For example, can you tell me how many instances of any single file is being stored across your organization? Probably not.  But if it’s being backed up to a single-instance repository, the repository stores a single copy of that file object, and the index in the repository has the links and metadata about where the file came from and how many redundant copies exist. By simply providing a search function into the repository, you would instantly be able to find out how many duplicate copies exist for every file you are backing up, and where they are coming from. Know
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