We performed a comparison between Apache Spark and Netezza Analytics based on real PeerSpot user reviews.
Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The scalability has been the most valuable aspect of the solution."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The processing time is very much improved over the data warehouse solution that we were using."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The most valuable feature is the performance."
"Speed contributes to large capacity."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"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."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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 need for administration involvement is quite limited on the solution."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"It should support more programming languages."
"Apache Spark should add some resource management improvements to the algorithms."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"The Analytics feature should be simplified."
"The hardware has a risk of failure. They need to improve this."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The solution could implement more reporting tools and networking utilities."
Apache Spark is ranked 1st in Hadoop with 60 reviews while Netezza Analytics is ranked 11th in Hadoop. Apache Spark is rated 8.4, while Netezza Analytics is rated 7.4. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". 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". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas Netezza Analytics is most compared with Spark SQL and HPE Ezmeral Data Fabric. See our Apache Spark vs. Netezza Analytics report.
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