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."I found the solution stable. We haven't had any problems with it."
"There's a lot of functionality."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"We use Spark to process data from different data sources."
"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."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"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."
"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 product could improve the user interface and make it easier for new users."
"One limitation is that not all machine learning libraries and models support it."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"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."
"The solution must improve its performance."
"Apache Spark provides very good performance The tuning phase is still tricky."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"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."
"The solution could implement more reporting tools and networking utilities."
"The hardware has a risk of failure. They need to improve this."
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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