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Amazon EMR is the extremely large datasets transformation solution
December 10, 2021

Amazon EMR is the extremely large datasets transformation solution

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Amazon EMR (Elastic MapReduce)

Most commonly we use Amazon ec2 for daily work on the cloud but when it comes to pulling large amounts of data and performing large transformations using spark, we would use the amazon EMR service to get that work done. The usage is limited to a few departments rather than the entire company.
  • Quick.
  • Massive.
  • Relatively cheap.
  • Harder to manager than ec2.
  • Could be very costly if not doing right.
  • RAM is fixed.
  • Usage monitoring.
  • Customizable master node.
  • Customizable worker nodes.
  • Big price on failed instance.
  • Save time on big dataset.
  • Monitoring usage.
Compared to Databricks, Amazon EMR is a much cheaper option to get the work down. And compared to Amazon ec2, Amazon EMR is a much more powerful tool to get large datasets transformation down in a fairly short amount of time. The drawback is that amazon EMR would be very costly if the run failed.

Do you think Amazon EMR (Elastic MapReduce) delivers good value for the price?

Yes

Are you happy with Amazon EMR (Elastic MapReduce)'s feature set?

Yes

Did Amazon EMR (Elastic MapReduce) live up to sales and marketing promises?

Yes

Did implementation of Amazon EMR (Elastic MapReduce) go as expected?

Yes

Would you buy Amazon EMR (Elastic MapReduce) again?

Yes

It has its use case. When it comes to extremely large datasets' pulling and transformation, it is the best way to do it. Just need to be aware that if something is not set correctly in the first place, the cost could potentially be very large. Therefore, it is very critical to not do development work there.