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."The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The solution has been very stable."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"Provides a lot of good documentation compared to other solutions."
"The solution is very stable."
"Spark can handle small to huge data and is suitable for any size of company."
"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 most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"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."
"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."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"There were some problems related to the product's compatibility with a few Python libraries."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"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."
"The product could improve the user interface and make it easier for new users."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"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."
"The hardware has a risk of failure. They need to improve this."
"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."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
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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