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 8.3, while Dremio is ranked #8 with an average rating of 9.0. Databricks holds a 19.1% mindshare in Data Science Platforms, compared to Dremio’s 4.3% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Dremio users who would recommend it.

Databricks

Read 85 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

Dremio

Read 7 Dremio reviews

2,663 Views
2,000 Comparison Views

100% willing to recommend

Databricks

Dremio

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Executive SummaryUpdated on Jan 12, 2025

Databricks and Dremio compete in the data analytics platform category. Databricks seems to have the upper hand due to its robust support for machine learning and advanced integrations.

Features: Databricks offers efficient cluster management, support for multiple programming languages, and integration with Delta Lake for enhanced data processing. Its strengths include scalability and built-in machine learning capabilities. Dremio provides effective data lineage and accommodates various data storage options like Amazon S3. Its standout feature is the ability to manage data provenance efficiently.

Room for Improvement: Databricks users seek improved visualization capabilities, expanded library compatibility for machine learning, and clearer error messaging. There is also demand for better integration with tools like Power BI and Tableau. Dremio can enhance its SQL efficiency and better integrate with analytical tools, with users citing issues in executing complex queries and limited support for recursive SQL expressions.

Ease of Deployment and Customer Service: Databricks is highly used across cloud environments with mixed feedback on customer support; some users find it responsive while others face delays. Dremio sees less frequent deployment across environments but receives praise for clear documentation, although direct support engagement is reportedly less common.

Pricing and ROI: Databricks is perceived as costly, with fluctuations based on compute and storage consumption, yet shows strong ROI potential when optimized. Dremio is more cost-effective compared to platforms like Snowflake but criticized for expensive licensing beyond the community version. Both solutions emphasize careful resource management to maximize financial benefits.

To learn more, read our detailed Databricks vs. Dremio Report (Updated: January 2025).

Buyer's Guide

Databricks vs. Dremio

January 2025

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Review summaries and opinions

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

Categories and Ranking

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Streaming Analytics (1st)

Dremio

Ranking in Data Science Platforms

8th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (10th)

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. The mindshare of Dremio is 4.3%, up from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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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.

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Quotes from Members

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

Pros

"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."

"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."

"The solution is easy to use and has a quick start-up time due to being on the cloud."

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

"The processing capacity is tremendous in the database."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

"The ease of use and its accessibility are valuable."

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

More Databricks pros

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

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

"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 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."

"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."

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

Cons

"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."

"Databricks' technical support takes a while to respond and could be improved."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"Databricks could improve in some of its functionality."

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"It would be nice to have more guidance on integrations with ETLs and other data quality tools."

"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."

"Costs can quickly add up if you don't plan for it."

More Databricks cons

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

"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."

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

"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."

"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."

"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."

Pricing and Cost Advice

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"I would rate Databricks' pricing seven out of ten."

"The solution is affordable."

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

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"Databricks are not costly when compared with other solutions' prices."

"The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive."

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

More Databricks pricing and cost advice

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

32%

Computer Software Company

10%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 Python. It offers many different cluster choices and excellent integration with ...

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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 designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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 analytics teams that have to interpret data to further the business goals of their orga...

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 on single nodes using a community version. We face certain issues when connectin...

See all answers

Comparisons

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 13% of the time

Starburst Enterprise vs Dremio

Compared 7% 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

Databricks

January 2025

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Buyer's Guide

Dremio

January 2025

Download Dremio product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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Databricks

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Dremio

Overview

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

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

UBS, TransUnion, Quantium, Daimler, OVH

Buyer's Guide

Databricks vs. Dremio

January 2025

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