We performed a comparison between Amazon EMR and Netezza Analytics based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"The initial setup is pretty straightforward."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"Amazon EMR is a good solution that can be used to manage big data."
"The solution is pretty simple to set up."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"It is a back end for our SSIS, MicroStrategy,, Tableau. All of these are connecting to get the data. To do so we are also using our analytics which is built on the data."
"The performance of the solution is its most valuable feature. The solution is easy to administer as well. It's very user-friendly. On the technical side, the architecture is simple to understand and you don't need too many administrators to handle the solution."
"The most valuable feature is the performance."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"Speed contributes to large capacity."
"The need for administration involvement is quite limited on the solution."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The legacy versions of the solution are not supported in the new versions."
"There is room for improvement in pricing."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The product must add some of the latest technologies to provide more flexibility to the users."
"The problem for us is it starts very slow."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"Modules and strategies should be better handled and notified early in advance."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The hardware has a risk of failure. They need to improve this."
"The most valuable features of this solution are robustness and support."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"The solution could implement more reporting tools and networking utilities."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"The Analytics feature should be simplified."
Amazon EMR is ranked 3rd in Hadoop with 20 reviews while Netezza Analytics is ranked 11th in Hadoop. Amazon EMR is rated 7.8, while Netezza Analytics is rated 7.4. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Netezza Analytics writes "ARULES() function is the fastest implementation of the associations algorithm (a priori or tree) I have worked with". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Apache Spark, whereas Netezza Analytics is most compared with Spark SQL and HPE Ezmeral Data Fabric.
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