BigQuery vs Dremio comparison

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BigQuery

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Executive SummaryUpdated on Nov 9, 2025

Dremio and BigQuery compete in the data analytics and warehousing category. BigQuery seems to have the upper hand due to its robust speed, scalability, and seamless integration capabilities.

Features: Dremio supports Apache Airflow and offers extensive data lineage capabilities, easily integrating with diverse storage solutions. It facilitates advanced analytics without impacting original data. BigQuery provides fast data processing, seamless GCP integration, and robust storage. Its serverless architecture supports machine learning models, optimizing large-scale data handling with exceptional speed and performance.

Room for Improvement: Dremio needs better Delta connector support, enhanced documentation for its community version, and improved SQL capabilities. It also faces challenges in complex query execution and authentication. BigQuery struggles with high pricing, special character restrictions, and cache limitations. Its ecosystem complexity results in a higher learning curve and a lack of local data centers in some regions.

Ease of Deployment and Customer Service: Dremio offers deployment on Public Cloud, Hybrid Cloud, and On-premises, though complex queries often require community support. Its customer service is generally responsive but faces challenges in scaling. BigQuery's public cloud setup is straightforward, supported by robust customer service, leading to high user satisfaction. Dremio provides broader deployment options, while BigQuery excels in cloud integration and support.

Pricing and ROI: Dremio is cost-effective, with savings in manpower and integration justifying its pricing, although its licensing can be expensive. BigQuery offers flexible pricing models, effective for storage but potentially costly for data processing. Its consumption-based pricing demands careful management to avoid high costs. Dremio stands out with competitive pricing, whereas BigQuery offers flexible yet potentially pricey consumption-based costs.

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

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BigQuery vs. Dremio

December 2025

Helped 881,082 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:

ROI

Sentiment score

5.8

BigQuery provides 27% better query performance and 17% cost reduction, offering seamless integration and efficient data management with training.

Sentiment score

5.6

Dremio reduces manpower costs, enhances efficiency, and eliminates infrastructure concerns, improving operations by accessing multiple data sources.

No quotes available

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Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.

For more quotes and insights, download the Dremio report

SR BI developer at BRQ Digital Solutions

Customer Service

Sentiment score

7.2

Google's support is rated 8/10 for responsiveness, but challenges exist in accessibility, integration, and documentation resources.

Sentiment score

5.2

Dremio's customer service is responsive and helpful, facing staffing challenges as demand grows, requiring more integrators for support.

I have been self-taught and I have been able to handle all my problems alone.

Chief Technical Lead at a consultancy with 201-500 employees

rating the customer support at ten points out of ten

For more quotes and insights, download the BigQuery report

Sr. Team Lead - IT at InfoStretch

I would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.

Mikah Sellers

Principal at Sgt Suds

We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.

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SR BI developer at BRQ Digital Solutions

Scalability Issues

Sentiment score

7.8

BigQuery excels in scalability and resource management, supporting large data with seamless auto-scaling despite some processing limitations.

Sentiment score

7.1

Dremio scales well, offering flexibility and built-in capabilities, though community users face scaling limits due to licensing.

It is a 10 out of 10 in terms of scalability.

For more quotes and insights, download the BigQuery report

Chief Technical Lead at a consultancy with 201-500 employees

The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

Dremio's scalability can handle growing data and user demands easily.

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SR BI developer at BRQ Digital Solutions

Internally, if it's on Docker or Kubernetes, scalability will be built into the system.

KamleshPant

Senior Software Architect at USEReady

Stability Issues

Sentiment score

8.3

BigQuery is a stable, efficient cloud analytics tool, with minor scalability challenges and reliable Google support for large-scale data handling.

Sentiment score

7.2

Dremio is generally stable, scoring high ratings with occasional performance issues, especially with large datasets, requiring maintenance restarts.

No quotes available

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I rate Dremio a nine in terms of stability.

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SR BI developer at BRQ Digital Solutions

Room For Improvement

BigQuery struggles with special character restrictions, high costs, complex optimization, and lacks user-friendly integration and performance flexibility.

Dremio struggles with Delta connector support, performance issues, SQL limitations, high costs, and fewer connectors than competitors.

Troubleshooting requires opening each pipeline individually, which is time-consuming.

Sr. Team Lead - IT at InfoStretch

BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.

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Chief Technical Lead at a consultancy with 201-500 employees

In general, if I know SQL and start playing around, it will start making sense.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

Starburst comes with around 50 connectors now.

KamleshPant

Senior Software Architect at USEReady

I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.

For more quotes and insights, download the Dremio report

SR BI developer at BRQ Digital Solutions

It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

Setup Cost

BigQuery provides flexible pricing with pay-as-you-go models, $300 credits, and competitive costs requiring mindful usage to control expenses.

Dremio's pricing, though costly for scaling, is seen as valuable compared to competitors, requiring careful evaluation based on needs.

Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.

Chief Technical Lead at a consultancy with 201-500 employees

The price is perceived as expensive, rated at eight out of ten in terms of costliness.

For more quotes and insights, download the BigQuery report

Sr. Team Lead - IT at InfoStretch

No quotes available

For more quotes and insights, download the Dremio report

Valuable Features

BigQuery excels in scalability, speed, and usability with serverless architecture, SQL support, and cost-effective data processing capabilities.

Dremio offers efficient data management and visualization with seamless integration, native SQL, and role-based access control features.

It is really fast because it can process millions of rows in just a matter of one or two seconds.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

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.

Chief Technical Lead at a consultancy with 201-500 employees

BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.

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Sr. Team Lead - IT at InfoStretch

Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.

Joao Silveira

Data Analyst at a insurance company with 501-1,000 employees

Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

You just get the source, connect the data, get visualization, get connected, and do whatever you want.

KamleshPant

Senior Software Architect at USEReady

For more quotes and insights, download the Dremio report

Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

4th

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

5th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Science Platforms (11th)

Mindshare comparison

As of January 2026, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.7%, up from 7.5% compared to the previous year. The mindshare of Dremio is 6.6%, down from 9.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
BigQuery	7.7%
Dremio	6.6%
Other	85.7%

Cloud Data Warehouse

Featured Reviews

Handles large data sets efficiently and offers flexible data management capabilities

Chief Technical Lead at a consultancy with 201-500 employees

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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Has simplified complex data integration workflows and supported consistent reporting across multiple sources

SR BI developer at BRQ Digital Solutions

We also have a close relationship with the team that does the Dremio maintenance for the database, like upgrading the versions and they know about some specific problems we had in the past, such as a memory leak. We had a memory leak on some versions, which sometimes stopped the service. Since we are using Dremio installed like a server, not a SaaS solution, many times we need to stop and restart the service to clear all the cache and all that, and this is the thing I should add. I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement. I remember using some features in the past, like pivot tables, which proved to be really difficult, but I know this is a fault also for other vendors. Pivoting, transposing, and unpivoting are often not so good. CTEs also many times prove to be not so good, so I think these two main items could be improved significantly if they standardize them.

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

13%

Retailer

Financial Services Firm

28%

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	5
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?

There are areas that could be improved with BigQuery, such as more bolt-on capabilities and the ability to use more bolt-ons for APIs. Having more of a library of connectors would be really benefic...

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?

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

What needs improvement with Dremio?

I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Compared 20% of the time

Teradata vs BigQuery

Compared 11% of the time

Snowflake vs BigQuery

Compared 11% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 8% of the time

Microsoft Azure Synapse Analytics vs BigQuery

Compared 4% of the time

More BigQuery Competitors

Databricks vs Dremio

Compared 33% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Dataiku vs Dremio

Compared 6% of the time

Starburst Galaxy vs Dremio

Compared 3% of the time

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Product Reports

Download BigQuery product report

BigQuery

January 2026

Download Dremio product report

January 2026

Also Known As

No data available

Dremio AWS - BYOL

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 offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.

Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.

What are Dremio's most valuable features?

Native Error Interfaces: Simplifies error detection and troubleshooting
Integration with Apache Airflow: Enhances workflow automation capabilities
Scalability: Effortlessly handles growing data demands
Role-Based Access Management: Provides secure data access control
Data Reflection: Optimizes query performance automatically
Query Federation: Supports queries across multiple endpoints seamlessly
Data Cataloging and Virtualization: Offers streamlined data organization

What benefits and ROI should users look for?

Reduced Data Storage Costs: Efficiently manages data to minimize storage expenses
Improved Query Performance: Enhances processing speed for large datasets
Flexibility in Integration: Easily connects with storage solutions like Oracle and MySQL
Enhanced Data Management: Supports comprehensive data analysis workflows

Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.

Sample Customers

Information Not Available

UBS, TransUnion, Quantium, Daimler, OVH