Top Rated Amazon EMR Alternatives
64 Amazon EMR Reviews
Overall Review Sentiment for Amazon EMR
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EMR we are using for running business logic on large business data received from various sources and third pary vendors Review collected by and hosted on G2.com.
Auto scaling for task and core nodes is slow and takes more than 15 minutes normally which causes failure of running jobs due to lack of resources on cluster. Review collected by and hosted on G2.com.
It is very easy to launch or clone EMR cluster. And EMR provides very easy scaling capabilities based on containers, cpu , spot instances, usage of insance fleet or instance groups . And EMR supports many of the widely used applications like Spark, Hive, Hadoop, Trino, Presto, Ranger , Flink etc Review collected by and hosted on G2.com.
Working with Spot instances on EMR is slightly complicated during unavailability of spot instances when you need to use instances on once particular availability zone. Many solutions like databricks provide fallback which are even more easy to use Review collected by and hosted on G2.com.
Amazon EMR is a much more powerful product to deploy big data solutions on top of Spark, Flink, scoop, etc. It is very is to configure and provides a nice UI that helps a lot in debugging spark jobs. Apart from that, from an observability point of view, EMR dumps all logs to S3 and the cloud watch which eventually helps developers to debug memory issues in the cluster Review collected by and hosted on G2.com.
I don't like the notebook interface it provides, it doesn't have features like auto-completion etc. Review collected by and hosted on G2.com.
Great User Experience and User Interface
Faster
More scalable
Can Automate Easily
Read input from multiple sources
Write output to multiple sources
Accepted different types of programming language Review collected by and hosted on G2.com.
Booting up takes time that needs more patience
great tool but expensive that is one of the main disadvantage
Other than that nothing, everything looks great, I am using it every day. Review collected by and hosted on G2.com.
My workloads run faster and I have more time to work on refining the code, instead of just sitting down waiting for the query to run Review collected by and hosted on G2.com.
It's not as elastic as promoted. I would like the cluster to scale in real time Review collected by and hosted on G2.com.
> One of the multiclustered architectures for bigdata processing which includes all kind of files
> User does not need to worry about maintaience of the clustes and clusters will be dynamically replaced based on failure
> Its one of the architecutes for map reduce proceesing(hadoop processing), can process PB data using multithreaded architecure Review collected by and hosted on G2.com.
> its not serverless, i.e admin/user need to manually provision the clusters for processing, and to be deleted after processing
> Cost is comparatively more in comparision with serverless services available in AWS
> EMR comes with 2 master nodes, if both master nodes fails, EMR cluster will go down, i.e users need to provision for multi az cluster to avoid node failures Review collected by and hosted on G2.com.
Ease of creating EMR instances and choosing required Softwares to have reinstalled (Spark/Hive/etc.). Review collected by and hosted on G2.com.
Cost is somewhat high and can be a limiting factor sometimes Review collected by and hosted on G2.com.
User customisable plans and cost efficiency, and best UI ofcourse. Review collected by and hosted on G2.com.
Cloud processes are still slow as it's virtual, but still it's a better choice Review collected by and hosted on G2.com.
No traditional multiprocess is required, distributes the work between the client node, best and earlier work found was using pspark with a data frame with a high level of python APIs Review collected by and hosted on G2.com.
The platform is tightly packed, it will be difficult to do configuration changes on YAML files Review collected by and hosted on G2.com.
