Databricks vs Dremio comparison

Databricks and Dremio are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.2, while Dremio is ranked #8 with an average rating of 9.0. Databricks holds a 19.2% mindshare in Data Science Platforms, compared to Dremio’s 4.2% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Dremio users who would recommend it.

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Oct 8, 2024

Databricks and Dremio are two prominent data platforms in the market. Databricks holds the upper hand with user satisfaction in support and flexibility, though Dremio excels in performance and scalability.

Features: Databricks offers integration capabilities, flexibility in handling large-scale data, and enhances collaborative analytics. It also provides a comprehensive ecosystem beneficial for various analytics tasks. Dremio boosts performance with accelerated query execution and enhances data lake exploration. It offers seamless BI tool integrations and effective performance optimization.

Room for Improvement: Databricks could improve documentation clarity, address occasional stability issues, and refine user onboarding experiences. Dremio can enhance user onboarding, expand community support, and improve user assistance resources.

Ease of Deployment and Customer Service: Databricks provides a versatile deployment model backed by robust but occasionally technical customer support. Dremio is easy and fast to deploy, with clear setup instructions and room for improving support interactions.

Pricing and ROI: Databricks is viewed as a premium option with advanced features providing strong ROI in complex environments. Dremio is cost-effective at initial setup and delivers performance value at a lower cost, although ROI might take longer to realize.

To learn more, read our detailed Databricks vs. Dremio Report (Updated: December 2024).

Buyer's Guide

Databricks vs. Dremio

December 2024

Download the complete report

Helped 824,053 peers since 2012

Categories and Ranking

IBM SPSS Statistics

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 Databricks is 19.2%, up from 18.7% compared to the previous year. The mindshare of Dremio is 4.2%, up from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

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.

Read full review

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

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.

Read full review

MikeWalker

Founder/ CEO at Morphogen Data Inc.

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.

Read full review

Quotes from Members

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

Pros

"SPSS is quite robust and quicker in terms of providing you the output."

"IBM SPSS Statistics depends on AI."

"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."

"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."

"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."

"It has the ability to easily change any variable in our research."

"The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."

"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."

More IBM SPSS Statistics pros

"I like cloud scalability and data access for any type of user."

"The integration with Python and the notebooks really helps."

"The simplicity of development is the most valuable feature."

"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."

"I work in the data science field and I found Databricks to be very useful."

"It offers AI functionalities that assist with code management and machine learning processes."

"Databricks helps crunch petabytes of data in a very short period of time."

"The solution is an impressive tool for data migration and integration."

More Databricks pros

"We primarily use Dremio to create a data framework and a data queue."

"Everyone uses Dremio in my company; some use it only for the analytics function."

"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."

"Dremio is very easy to use for building queries."

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

"Dremio allows querying the files I have on my block storage or object storage."

"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."

Cons

"IBM SPSS Statistics could improve the visual outputs where you are producing, for example, a graph for a company board of directors, or an advert."

"It could provide even more in the way of automation as there are many opportunities."

"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."

"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."

"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The design of the experience can be improved."

"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."

More IBM SPSS Statistics cons

"Can be improved by including drag-and-drop features."

"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."

"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."

"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."

"Databricks could improve in some of its functionality."

"The connectivity with various BI tools could be improved, specifically the performance and real time integration."

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

More Databricks cons

"It shows errors sometimes."

"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."

"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."

"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

Pricing and Cost Advice

"The price of IBM SPSS Statistics could improve."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"We think that IBM SPSS is expensive for this function."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"I rate the tool's pricing a five out of ten."

"The price of this solution is a little bit high, which was a problem for my company."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

More IBM SPSS Statistics pricing and cost advice

"The billing of Databricks can be difficult and should improve."

"There are different versions."

"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"The solution is affordable."

"We're charged on what the data throughput is and also what the compute time is."

"The price of Databricks is reasonable compared to other solutions."

"The cost is around $600,000 for 50 users."

More Databricks pricing and cost advice

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"Dremio is less costly competitively to Snowflake or any other tool."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

824,053 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

16%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

32%

Computer Software Company

11%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

See all answers

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...

See all answers

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...

See all answers

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 ...

See all answers

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...

See all answers

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...

See all answers

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

See all answers

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.

See all answers

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 ...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 11% of the time

Amazon SageMaker vs IBM SPSS Statistics

Compared 6% of the time

More IBM SPSS Statistics Competitors

Dataiku vs Databricks

Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 8% of the time

Microsoft Power BI vs Dremio

Compared 4% of the time

Amazon Redshift vs Dremio

Compared 3% of the time

Dataiku vs Dremio

Compared 3% of the time

More Dremio Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Databricks

December 2024

Download Databricks product report

Buyer's Guide

Dremio

December 2024

Download Dremio product report

Also Known As

SPSS Statistics

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Learn More

Video not available

IBM

Databricks

Video not available

Dremio

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.

At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.

What Are Dremio's Key Features?

Data Reflections: Reduces query times by creating optimized representations of source data, which can accelerate performance without the complexity of traditional data warehousing solutions.
Semantic Layer: Allows users to define business metrics and dimensions centrally, ensuring consistency and governance across all analytics tools.
Built-in Security Features: Provides robust security measures, including column- and row-level security, ensuring compliance with data governance and privacy standards.
Support for Multiple Data Formats and Sources: Enables querying directly against a variety of data formats (Parquet, JSON, etc.) and sources without the need for conversion or replication.

What Benefits Should Users Expect?

When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.

How is Dremio Implemented Across Different Industries?

Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.

What About Dremio’s Pricing, Licensing, and Support?

Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.

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

Buyer's Guide

Databricks vs. Dremio

December 2024

Free Report: Databricks vs. Dremio

Find out what your peers are saying about Databricks vs. Dremio and other solutions. Updated: December 2024.

DOWNLOAD NOW

824,053 professionals have used our research since 2012.

See our Databricks vs. Dremio report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms 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.