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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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Hadoop 2x vs 3x top differences

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In many interviews, the first question for Hadoop developers is what are the differences between Hadoop 2 and 3. You already know that Hadoop upgraded from version 1.

Hadoop features


The below list is useful to know the differences. I have given Hadoop details in the form of questions and answers so that beginners can understand.

Hadoop 2.x Vs 3.x


hadoop v2 vs 3
The major change in hadoop 3 is no storage overhead. So, you may be curious about how Hadoop 3 is managing storage.

My plan is for you is first to go through the list of differences and check the references section, to learn more about Hadoop storage management.

References

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