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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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Top requirements for successful MapReduce jobs

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The following techniques are needed to be successful of your map reduce jobs:
  • The mapper must be able to ingest the input and process the input record, sending forward the records that can be passed to the reduce task or to the final output directly, if no reduce step is required.
Mapreduce Jobs in Hadoop
Hadoop-MapReduce
  • The reducer must be able to accept the key and value groups that passed through the mapper, and generate the final output of this MapReduce step.
  • The job must be configured with the location and type of the input data, the mapper class to use, the number of reduce tasks required, and the reducer class and I/O types.
  • The TaskTracker service will actually run your map and reduce tasks, and the JobTracker service will distribute the tasks and their input split to the various trackers.
  • The cluster must be configured with the nodes that will run the TaskTrackers, and with the number of TaskTrackers to run per node. The TaskTrackers need to be configured with the JVM parameters, including the classpath for both the TaskTracker and the JVMs that will execute the individual tasks.
  • There are three levels of configuration to address to configure MapReduce on your cluster. From the bottom up, you need to configure the machines, the Hadoop MapReduce framework, and the jobs themselves.
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