Try our new research platform with insights from 80,000+ expert users

BigQuery vs Dremio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

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
7.2
Number of Reviews
41
Ranking in other categories
No ranking in other categories
Dremio
Ranking in Cloud Data Warehouse
7th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
8
Ranking in other categories
Data Science Platforms (9th)
 

Mindshare comparison

As of July 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 6.5%, down from 8.4% compared to the previous year. The mindshare of Dremio is 10.7%, up from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Luís Silva - PeerSpot reviewer
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.
KamleshPant - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."
"Its integration with other tools like Atlan through a Google Chrome extension is highly beneficial."
"BigQuery has a very nice interface that you can easily learn if you know SQL."
"It's similar to a Hadoop cluster, except it's managed by Google."
"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."
"The initial setup is straightforward."
"We like the machine learning features and the high-performance database engine."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"Dremio is very easy to use for building queries."
"We primarily use Dremio to create a data framework and a data queue."
"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."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"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

"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."
"The solution hinges on Google patterns so continued improvement is important."
"BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses."
"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."
"BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement."
"Sometimes, support specialists might not have enough experience or business understanding, which can be an issue."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"It shows errors sometimes."
"They need to have multiple connectors."
"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
"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."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"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."
 

Pricing and Cost Advice

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."
"Price-wise, I think that is very reasonable."
"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."
"The tool has competitive pricing."
"The solution's pricing is cheaper compared to other solutions."
"BigQuery pricing can increase quickly. It's a high-priced solution."
"Its cost structure operates on a pay-as-you-go model."
"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."
"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."
report
Use our free recommendation engine to learn which Cloud Data Warehouse solutions are best for your needs.
860,632 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
16%
Manufacturing Company
11%
Retailer
8%
Financial Services Firm
30%
Computer Software Company
9%
Manufacturing Company
6%
Healthcare Company
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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...
 

Comparisons

 

Overview

 

Sample Customers

Information Not Available
UBS, TransUnion, Quantium, Daimler, OVH
Find out what your peers are saying about BigQuery vs. Dremio and other solutions. Updated: June 2025.
860,632 professionals have used our research since 2012.