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."The main feature that we find valuable is that it is very fast."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"The product’s most valuable features are lazy evaluation and workload distribution."
"I feel the streaming is its best feature."
"The product's deployment phase is easy."
"It provides a scalable machine learning library."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"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."
"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."
"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."
"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."
"Speed contributes to large capacity."
"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."
"The setup I worked on was really complex."
"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."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"It should support more programming languages."
"Apache Spark's GUI and scalability could be improved."
"There were some problems related to the product's compatibility with a few Python libraries."
"The most valuable features of this solution are robustness and support."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"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 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."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
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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