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Dremio vs Shakudo Platform comparison

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Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
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Ranking in Data Science Platforms
9th
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Dremio
Ranking in Data Science Platforms
8th
Average Rating
8.6
Reviews Sentiment
7.2
Number of Reviews
7
Ranking in other categories
Cloud Data Warehouse (10th)
Shakudo Platform
Ranking in Data Science Platforms
33rd
Average Rating
0.0
Number of Reviews
0
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Dremio is 4.2%, up from 2.3% compared to the previous year. The mindshare of Shakudo Platform is 0.2%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Md Masudul Hassan - PeerSpot reviewer
Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts
I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.
MikeWalker - PeerSpot reviewer
It enables you to manage changes more effectively than any other platform.
Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it. There's another thing called data providence. They're tied together. Data providence allows you to go back and recreate the data at any particular point in time. It's extremely important for compliance and governance issues because data changes all time. How did it change? What was it three days or months ago? You may have made some decisions based on data that was three months old, so you might need to revisit those. It's essential for things like machine learning and deep learning, where you are generating AI models off data. When the model stops working or doesn't work as expected, you need to figure out why. You have to go back and adjust the datasets used to train the model. We do that through an open-source project called Nessie, which is their basis for providing data lineage and data province capabilities. It's super powerful. Arrow is another open-source project for storing data in memory and performing data query operations. Data sits on a disk in one format. If you want to do anything with data, you have to load it into your computer and put it into memory so you can work with it. Arrow provides a format in memory that enables the whole library to perform various operations on that data. Every vendor has its own way of representing data in memory. They've latched onto an industry standard and developed it so it's open. Now people can use the exact same format in memory to do operations and use the library set to perform functions on data. New developers can decide if they want to develop their own memory format or use one that's already there. Data transfer is a massive problem when you're working with large datasets, doing advanced analytics, and trying to train machine learning or deep learning models. What happens often is companies downsample their data sets to do training on models because transferring and managing data on a deep learning or machine learning platform is too much.
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
9%
University
8%
Manufacturing Company
8%
Financial Services Firm
32%
Computer Software Company
11%
Manufacturing Company
8%
Retailer
4%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about IBM SPSS Statistics?
The software offers consistency across multiple research projects helping us with predictive analytics capabilities.
What is your experience regarding pricing and costs for IBM SPSS Statistics?
The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...
What needs improvement with IBM SPSS Statistics?
IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...
What do you like most about Dremio?
Dremio allows querying the files I have on my block storage or object storage.
What is your experience regarding pricing and costs for Dremio?
The licensing is very expensive. We need a license to scale as we are currently using the community version.
What needs improvement with Dremio?
There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it ...
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Also Known As

SPSS Statistics
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Overview

 

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
UBS, TransUnion, Quantium, Daimler, OVH
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