BigQuery vs Dremio comparison

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BigQuery

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Executive SummaryUpdated on Dec 18, 2024

Dremio and BigQuery are key players in the cloud data analytics space. Dremio is often chosen for its ease of integration and user-friendliness, while BigQuery's comprehensive feature set and scalability give it a competitive edge despite potential higher costs.

Features: Dremio offers native integration with various data sources and executes queries with high performance, along with flexible data transformations. BigQuery is known for its powerful querying capabilities, scalability, and seamless integration within Google's ecosystem.

Room for Improvement: Dremio could enhance its scalability options and provide more advanced analytics capabilities. BigQuery might work on reducing costs associated with its use and improving its real-time processing capabilities. Both platforms can benefit from more intuitive user interfaces.

Ease of Deployment and Customer Service: BigQuery benefits from Google's infrastructure, ensuring solid scalability and reliability while offering comprehensive support. Dremio provides a straightforward deployment process with detailed documentation and community support, appealing to companies seeking quick setups.

Pricing and ROI: Dremio is recognized for its competitive pricing, offering a good ROI through efficient performance. BigQuery, while potentially more costly upfront, delivers significant ROI due to its extensive capabilities and scalability, appealing to those seeking long-term value.

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

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BigQuery vs. Dremio

September 2025

Helped 872,706 peers since 2012

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

BigQuery

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Dremio

Ranking in Cloud Data Warehouse

6th

Average Rating

8.4

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Science Platforms (10th)

Mindshare comparison

As of October 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 8.0%, up from 7.8% compared to the previous year. The mindshare of Dremio is 8.9%, up from 8.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
BigQuery	8.0%
Dremio	8.9%
Other	83.1%

Cloud Data Warehouse

Featured Reviews

Luís Silva

Chief Technical Lead at a consultancy with 201-500 employees

Handles large data sets efficiently and offers flexible data management capabilities

The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.

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KamleshPant

Senior Software Architect at USEReady

Solution offers quick data connection with an edge in computation

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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Quotes from Members

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

Pros

"The initial setup is straightforward."

"Its integration with other tools like Atlan through a Google Chrome extension is highly beneficial."

"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."

"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."

"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

"BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data."

More BigQuery pros

"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."

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

"Dremio has positively impacted my organization by helping us create a single source of truth, a singular data warehouse where we can have access to all of the data sets."

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

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

"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."

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

More Dremio pros

Cons

"The processing capability can be an area of improvement."

"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."

"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."

"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."

"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."

"It would be helpful if they could provide some dashboards where you can easily view charts and information."

"There are some limitations in the query latency compared to what it was three years ago."

"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."

More BigQuery cons

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

"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."

"We had a memory leak on some versions, which sometimes stopped the service."

"They need to have multiple connectors."

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

"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."

More Dremio cons

Pricing and Cost Advice

"The platform is inexpensive."

"The pricing is good and there are no additional costs involved."

"Price-wise, I think that is very reasonable."

"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."

"BigQuery pricing can increase quickly. It's a high-priced solution."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

More BigQuery 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

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

12%

Retailer

Financial Services Firm

29%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	9
Large Enterprise	20

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	4
Large Enterprise	5

Questions from the Community

What do you like most about BigQuery?

The initial setup process is easy.

What is your experience regarding pricing and costs for BigQuery?

I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...

What needs improvement with BigQuery?

I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery compon...

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?

They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst do...

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Compared 15% of the time

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Compared 7% of the time

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Compared 6% of the time

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Compared 4% of the time

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Product Reports

Download BigQuery product report

BigQuery

October 2025

Download Dremio product report

October 2025

Overview

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

Google

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

Information Not Available

UBS, TransUnion, Quantium, Daimler, OVH