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 Spark to process data from different data sources."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"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."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The most valuable feature of Apache Spark is its flexibility."
"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."
"Provides a lot of good documentation compared to other solutions."
"The product's deployment phase is easy."
"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."
"Speed contributes to large capacity."
"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."
"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."
"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."
"At the initial stage, the product provides no container logs to check the activity."
"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."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The product could improve the user interface and make it easier for new users."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"The most valuable features of this solution are robustness and support."
"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."
"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."
"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."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
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 list of best Hadoop vendors.
We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
