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 solution is scalable."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"I found the solution stable. We haven't had any problems with it."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"Spark can handle small to huge data and is suitable for any size of company."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"Speed contributes to large capacity."
"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."
"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."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"At the initial stage, the product provides no container logs to check the activity."
"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 solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"The initial setup was not easy."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"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."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The Analytics feature should be simplified."
"The hardware has a risk of failure. They need to improve this."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"The most valuable features of this solution are robustness and support."
"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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