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."We use it for ETL purposes as well as for implementing the full transformation pipelines."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"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."
"ETL and streaming capabilities."
"The solution is very stable."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"Apache Spark can do large volume interactive data analysis."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"The most valuable feature is the performance."
"Speed contributes to large 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 setup I worked on was really complex."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Apache Spark should add some resource management improvements to the algorithms."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"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 times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"The product could improve the user interface and make it easier for new users."
"The hardware has a risk of failure. They need to improve this."
"The most valuable features of this solution are robustness and support."
"The Analytics feature should be simplified."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The solution could implement more reporting tools and networking utilities."
"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."
"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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