Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 28 discussion

A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day. • Partitioning data in Amazon S3 can significantly improve query performance. By organizing the data by year, month, and day, AWS Glue and Amazon QuickSight can scan only the relevant partitions of data, which reduces the amount of data read and processed. This approach is particularly effective for time-series data, where queries often target specific time ranges. B. Increase the AWS Glue instance size by scaling up the worker type. • Scaling up the worker type can provide more computational resources to the AWS Glue jobs, enabling them to process data faster. This can be especially beneficial when dealing with large datasets or complex transformations. It’s important to monitor the performance improvements and cost implications of scaling up.

1. **Partition the Data in Amazon S3**: - AWS documentation on optimizing Amazon S3 performance: https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimizing-performance.html - AWS Glue documentation on partitioning data for AWS Glue jobs: https://docs.aws.amazon.com/glue/latest/dg/how-it-works.html#how-partitioning-works - Best practices for partitioning in Amazon S3: https://docs.aws.amazon.com/AmazonS3/latest/userguide/best-practices-partitioning.html 2. **Optimizing AWS Glue Job Settings**: - AWS Glue documentation on optimizing job performance: https://docs.aws.amazon.com/glue/latest/dg/best-practices.html - AWS Glue documentation on scaling AWS Glue job resources: https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-glue-job-cloudwatch-metrics.html By referring to these documentation resources, the data engineer can gain insights into best practices and recommendations provided by AWS for optimizing AWS Glue jobs, thereby justifying the suggested actions to address the issue of slowing job performance.