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Databricks vs Dremio comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Oct 8, 2024

Review summaries and opinions

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

Customer Service

No sentiment score available
Sentiment score
6.8
Databricks support is praised for technical expertise and engagement, but experiences vary due to response times and Microsoft partner handling.
No sentiment score available
 

Room For Improvement

No sentiment score available
Sentiment score
5.8
Databricks faces challenges with visualization, integration, costs, error clarity, libraries, interfaces, documentation, onboarding, automation, governance, and performance.
No sentiment score available
 

Scalability Issues

No sentiment score available
Sentiment score
8.2
Databricks offers significant, praised scalability from megabytes to petabytes, supporting vertical and horizontal scaling with auto-scaling features.
No sentiment score available
 

Setup Cost

No sentiment score available
Sentiment score
7.0
Databricks pricing varies greatly based on usage and cluster type, often considered expensive with additional cloud storage costs.
No sentiment score available
 

Stability Issues

No sentiment score available
Sentiment score
7.7
Databricks is highly stable and reliable, with minimal issues reported, especially during heavy processes, and receives high user ratings.
No sentiment score available
 

Valuable Features

No sentiment score available
Sentiment score
8.5
Databricks offers user-friendly large-scale analytics, seamless integration, versatile coding, collaborative tools, and efficient big data handling with extensive cloud support.
No sentiment score available
 

Categories and Ranking

IBM SPSS Statistics
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Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Databricks
Ranking in Data Science Platforms
1st
Average Rating
8.2
Reviews Sentiment
7.4
Number of Reviews
84
Ranking in other categories
Streaming Analytics (1st)
Dremio
Ranking in Data Science Platforms
8th
Average Rating
8.6
Reviews Sentiment
5.9
Number of Reviews
6
Ranking in other categories
Cloud Data Warehouse (10th)
 

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Databricks is 19.1%, up from 19.1% compared to the previous year. The mindshare of Dremio is 4.1%, up from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
Enhancing survey analysis that provides valued insightfulness
I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.
Dunstan Matekenya - PeerSpot reviewer
Process large-scale data sets and integrates with Apache Spark with notebook environment
Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.
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%
University
9%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
16%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
32%
Computer Software Company
10%
Manufacturing Company
8%
Retailer
4%
 

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...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designe...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analyti...
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?
Every tool has a value based on its visualization, and the pricing is worth its value.
What needs improvement with Dremio?
Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I c...
 

Also Known As

SPSS Statistics
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
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Learn More

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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
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
UBS, TransUnion, Quantium, Daimler, OVH
Find out what your peers are saying about Databricks vs. Dremio and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.