Amazon EMR is ideal for Hadoop-based processing
April 22, 2022
Amazon EMR is ideal for Hadoop-based processing
Score 8 out of 10
Vetted Review
Verified User
Overall Satisfaction with Amazon EMR (Elastic MapReduce)
The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need to process a large amount of data across several EC2 servers, our DevOps team implements it. For our customers, EMR is attractive since it is far less expensive to adopt than alternative solutions, which means that the overall cost savings are substantial.
Pros
- Faster than prior on-premise systems to put in place.
- Open source software is supported.
- Reduces the cost of production.
Cons
- Automation of processing jobs creation and deletion.
- The cost of this service is more expensive than similar ones.
- Getting everything up and running at the beginning is a lengthy process.
- Compatibility between Spark and Hive workloads in Hadoop.
- EC2 Cluster Monitoring and Logging is at least five times slower than our data processing.
- Cost reductions for consumers are enormous as compared to other (more conventional) choices, particularly on-premise ones.
- S3 and EC2 API connection allowed for increased scalability.
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
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
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