Apache Spark vs Netezza Analytics comparison

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Executive Summary

We performed a comparison between Apache Spark and Netezza Analytics based on real PeerSpot user reviews.

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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.""The most valuable feature of Apache Spark is its ease of use.""I found the solution stable. We haven't had any problems with it.""Features include machine learning, real time streaming, and data processing.""The product is useful for analytics.""Provides a lot of good documentation compared to other solutions.""Apache Spark provides a very high-quality implementation of distributed data processing.""The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

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"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 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 need for administration involvement is quite limited on the solution.""Speed contributes to large capacity.""For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."

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Cons
"The solution’s integration with other platforms should be improved.""The setup I worked on was really complex.""The logging for the observability platform could be better.""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.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""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.""When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers."

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"The Analytics feature should be simplified.""The most valuable features of this solution are robustness and support.""In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there.""The solution could implement more reporting tools and networking utilities.""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.""Administration of this product is too tough. It's very complex because of the tools which it's missing.""This product is being discontinued from IBM, and I would like to have some kind of upgrade available."

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Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."
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  • "Expensive to maintain compared to other solutions."
  • "For me, mainly, it reduces my costs. It's not only the appliance cost. There are also support costs and a maintenance costs. It does reduce the costs very drastically."
  • "The annual licensing fees are twenty-two percent of the product cost."
  • More Netezza Analytics Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
    Top Answer:Here are some things to consider when migrating from Netezza to AWS Redshift A. Migrating your data from Netezza to Redshift may be done using methods such as: o Use a third-party tool to export… more »
    Ranking
    2nd
    out of 22 in Hadoop
    Views
    2,468
    Comparisons
    1,915
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    11th
    out of 22 in Hadoop
    Views
    235
    Comparisons
    100
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Learn More
    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    IBM Netezza Analytics is an embedded, purpose-built, advanced analytics platform that empowers analytic enterprises to meet and exceed their business demands. As features, it can predict with more accuracy, deliver predictions faster and respond rapidly to changes.
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    A leading online advertising network
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    No Data Available
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    REVIEWERS
    Small Business25%
    Midsize Enterprise17%
    Large Enterprise58%
    Buyer's Guide
    Hadoop
    March 2024
    Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Hadoop with 58 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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