Dremio Reviews

Name: Dremio
Brand: Dremio
Rating: 4.3 (8 reviews)

4.3 out of 5

8 reviews
100% willing to recommend

What is Dremio?

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.

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Dremio is the #8 ranked solution in top Data Science Platforms and #9 ranked solution in top Cloud Data Warehouse solutions. PeerSpot users give Dremio an average rating of 8.6 out of 10. Dremio is most commonly compared to Databricks: Dremio vs Databricks. Dremio is popular among the large enterprise segment, accounting for 70% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 31% of all views.

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Featured Dremio reviews

KamleshPant

Senior Software Architect at USEReady

It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want. They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.

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MikeWalker

Founder/ CEO at Morphogen Data Inc.

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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Vu Thang

Investment Banking Associate at Vietcap

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 connecting Dremio to MongoDB, especially with max values, which seem to be inconsistent in Dremio. Additionally, licensing is quite expensive, and we feel the need for more flexible schema capabilities, especially in embedding JSON from MongoDB.

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Dremio mindshare

Product category:

As of March 2025, the mindshare of Dremio in the Data Science Platforms category stands at 4.3%, up from 2.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

PeerAnalyst reports based on Dremio reviews

Type	Title	Date
Category	Data Science Platforms	Mar 4, 2025	Download
Product	Reviews, tips, and advice from real users	Mar 4, 2025	Download
Comparison	Dremio vs Databricks	Mar 4, 2025	Download
Comparison	Dremio vs KNIME	Mar 4, 2025	Download
Comparison	Dremio vs Amazon SageMaker	Mar 4, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	18.5%	96%	88 interviews Add to research
KNIME	4.0	11.6%	94%	59 interviews Add to research

Valuable Features

Dremio offers a native error interface and supports Apache Airflow, enabling data services without additional resources. It integrates with various data storages like Amazon S3 and Azure. Users appreciate its memory competition and ability to manage data lineage and providence. Dremio’s VDS and PDS creation improves data management. Its flexibility, ease of use, and scalability for queries across databases are highlighted. Arrow project aids in efficient data operations, optimizing machine learning processes.

"Overall, you can rate it as eight out of ten."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"Dremio is very easy to use for building queries."

Room for Improvement

Dremio lacks Delta connector support and has limited documentation. Users find performance slow for large queries and face integration issues with Databricks. They experience challenges with ANSI SQL functionalities, sorting complex data, and building hierarchical structures. The interface lacks efficiency in automated SQL tasks. Licensing is expensive, and there is demand for more connectors like Salesforce. Community users report recurring errors and difficulty scaling clusters without custom code. JSON embedding from MongoDB is problematic.

"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
"They need to have multiple connectors."
"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."

ROI

Dremio has significantly enhanced the speed and efficiency of data processing, leading to quicker data access and analysis. Many users highlighted the cost savings associated with reduced infrastructure needs due to Dremio's ability to query data directly from data lakes. The platform's ability to integrate with existing systems without extensive restructuring or additional storage costs was frequently appreciated.

Pricing

Dremio users indicate that the cluster costs about $200,000 monthly, depending on data volume. It is considered less expensive than competitors like Snowflake, yet licensing remains costly. Organizations note the necessity for a license to scale, as many currently rely on the community version. Pricing aligns with the tool's value and purpose, making it a worthwhile consideration despite the expense and licensing complexities.

"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."

Popular Use Cases

Primarily, Dremio is utilized for creating data marks on data lakes, serving as a query engine for data lake projects, and managing ad-hoc queries. It supports data framework construction and is often integrated with tools like DBT, Databricks, and BI platforms. Its application spans financial analytics and data engineering, and it enables visualization and processing across various databases. While migration and integration challenges exist, its sophisticated high-performance querying capabilities are valued.

Service and Support

Customers utilize the community forum for simple queries, though advanced questions require developer insights. Support assistance was contacted few times, with generally positive remarks. Dremio has a strong focus on enterprise customers, offering professional services and full assistance, yet faces staffing challenges as demand grows. Despite having a capable customer-centered team, more integrators are needed. Some users have not yet interacted with technical support, primarily using the platform for exploration or proof of concept.

Deployment

Dremio's initial setup varies in complexity based on use cases. For simple setups, it takes a day or two. Complex setups, like integrating with parquet tables, require experienced teams and a few days. Some found the process straightforward and quick, especially with cloud or containerized versions using Kubernetes. Integration with tools like DataDog may pose challenges. Cloud versions need minimal configuration, while on-premises setups involve basic installation without significant issues.

Scalability

Dremio is highly scalable, allowing users to adjust instances based on load and traffic needs. Configurations can be set during installation, supporting users across departments. Users appreciate its ability to handle massive data scales and integration with Docker and Kubernetes, making it adaptable for internal use. Even though some use the community version without scaling, many still rate its scalability highly, acknowledging its effectiveness for large-scale data operations.

Stability

Dremio is noted for its stability by multiple sources. Users commend its reliability, describing experiences as rock solid. Some highlight minor performance issues occasionally, which are sometimes attributed to limited experience. With a stable core, Dremio has provided a strong performance over three months, and several rate its stability highly. However, a few observe occasional errors impacting scalability. For smaller data tasks, it performs well, but larger data uses remain less explored.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Dremio Buyer's Guide for additional reliable information.

Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

31%

Computer Software Company

10%

Manufacturing Company

Retailer

Healthcare Company

University

Insurance Company

Government

Energy/Utilities Company

Media Company

Real Estate/Law Firm

Non Profit

Comms Service Provider

Recreational Facilities/Services Company

Transportation Company

Performing Arts

Educational Organization

Legal Firm

Hospitality Company

Pharma/Biotech Company

Construction Company

Outsourcing Company

Wholesaler/Distributor

Logistics Company

Engineering Company

Marketing Services Firm

Consumer Goods Company

Aerospace/Defense Firm

Compare Dremio with alternative products

Learn more about Dremio

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.