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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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SAP HANA: Top Data Processing Interview Questions

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1. How parallel processing is achieved in SAP HANA? The phrase "divide and conquer" (derived from the Latin saying divide et impera) typically is used when a large problem is divided into a number of smaller, easier-to-solve problems. Regarding performance, processing huge amounts of data is a problem that can be solved by splitting the data into smaller chunks of data, which can be processed in parallel. 2.How data portioning will happen in SAP HANA? Although servers that are available today can hold terabytes of data in memory and provide up to eight processors per server with up to 10 cores per processor, the amount of data that is stored in an in-memory database or the computing power that is needed to process such quantities of data might exceed the capacity of a single server. To accommodate the memory and computing power requirements that go beyond the limits of a single server, data can be divided into subsets and placed across a cluster of servers, which forms a d
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