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."Features include machine learning, real time streaming, and data processing."
"It provides a scalable machine learning library."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The main feature that we find valuable is that it is very fast."
"The solution has been very stable."
"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."
"The most valuable feature of Apache Spark is its ease of use."
"Speed contributes to large capacity."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"The need for administration involvement is quite limited on 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."
"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."
"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."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"It's not easy to install."
"One limitation is that not all machine learning libraries and models support it."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"Apache Spark's GUI and scalability could be improved."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"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."
"At the initial stage, the product provides no container logs to check the activity."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"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."
"The hardware has a risk of failure. They need to improve this."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"The most valuable features of this solution are robustness and support."
"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."
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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