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Apache Spark vs Netezza Analytics comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
64
Ranking in other categories
Compute Service (4th), Java Frameworks (2nd)
Netezza Analytics
Ranking in Hadoop
11th
Average Rating
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Hadoop category, the mindshare of Apache Spark is 18.0%, down from 21.8% compared to the previous year. The mindshare of Netezza Analytics is 1.5%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

SurjitChoudhury - PeerSpot reviewer
Offers batch processing of data and in-memory processing in Spark greatly enhances performance
Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.
Asish Govind - PeerSpot reviewer
Stable with limited administration needed but it's coming up on its end of life
I'm not sure if Netezza offers a cloud version of the solution or not, but if they don't they should. Most companies are focused on moving towards the cloud. If it was on a cloud it would offer certain scalability and performance aspects it can't offer as a physical appliance. I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"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."
"The solution is very stable."
"The data processing framework is good."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The scalability has been the most valuable aspect of the solution."
"The most valuable feature of Apache Spark is its flexibility."
"The deployment of the product is easy."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"Speed contributes to large capacity."
"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."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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 most valuable feature is the performance."
 

Cons

"There were some problems related to the product's compatibility with a few Python libraries."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"It's not easy to install."
"At the initial stage, the product provides no container logs to check the activity."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"Apache Spark provides very good performance The tuning phase is still tricky."
"They could improve the issues related to programming language for the platform."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"The most valuable features of this solution are robustness and support."
"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."
"The Analytics feature should be simplified."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The hardware has a risk of failure. They need to improve this."
 

Pricing and Cost Advice

"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."
"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"Spark is an open-source solution, so there are no licensing costs."
"The solution is affordable and there are no additional licensing costs."
"It is an open-source solution, it is free of charge."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"Apache Spark is an expensive solution."
"They provide an open-source license for the on-premise version."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
Retailer
5%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...
What needs improvement with Apache Spark?
The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...
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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
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Find out what your peers are saying about Apache Spark vs. Netezza Analytics and other solutions. Updated: December 2024.
824,067 professionals have used our research since 2012.