We performed a comparison between Apache Spark 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."There's a lot of functionality."
"I found the solution stable. We haven't had any problems with it."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The scalability has been the most valuable aspect of the solution."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The need for administration involvement is quite limited on the solution."
"The most valuable feature is the performance."
"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."
"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."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"One limitation is that not all machine learning libraries and models support it."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"They could improve the issues related to programming language for the platform."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"The most valuable features of this solution are robustness and support."
"The Analytics feature should be simplified."
"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."
"The hardware has a risk of failure. They need to improve this."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
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.
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