We performed a comparison between Netezza Analytics and Spark SQL based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."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."
"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."
"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."
"Speed contributes to large capacity."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The stability was fine. It behaved as expected."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"Data validation and ease of use are the most valuable features."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"This solution is useful to leverage within a distributed ecosystem."
"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."
"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."
"The Analytics feature should be simplified."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The most valuable features of this solution are robustness and support."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"In the next release, maybe the visualization of some command-line features could be added."
"There are many inconsistencies in syntax for the different querying tasks."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
"There should be better integration with other solutions."
Netezza Analytics is ranked 11th in Hadoop while Spark SQL is ranked 4th in Hadoop with 14 reviews. Netezza Analytics is rated 7.4, while Spark SQL is rated 7.8. 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". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Netezza Analytics is most compared with HPE Ezmeral Data Fabric, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, HPE Ezmeral Data Fabric and SAP HANA.
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