BigQuery vs Dremio comparison

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BigQuery

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Executive SummaryUpdated on Feb 15, 2026

Dremio and BigQuery are prominent competitors in the data processing and cloud infrastructure category. While both have their unique strengths, BigQuery frequently stands out for its scalable architecture and comprehensive machine learning features.

Features: Dremio offers versatility with federated queries and seamless integration with various data storage solutions such as Amazon S3. Its support for Apache Airflow and the ability to create virtual datasets enhances its functionality. BigQuery provides robust cloud storage and processing capabilities with a scalable framework, enabling complex queries and seamless machine learning integrations within its platform.

Room for Improvement: Dremio might struggle with large queries, and its Delta connector support remains limited. It also faces hurdles in performing advanced SQL operations and expanding integration capabilities. BigQuery, on the other hand, encounters restrictions with special characters during data migrations and caching limitations with external tables. Users may also find the learning curve steep in terms of setup and managing costs.

Ease of Deployment and Customer Service: Dremio supports multiple cloud environments and on-premises deployment but has been challenged by intermittent staffing issues in customer service. BigQuery thrives in public cloud applications and benefits from Google's managed services and enterprise-level customer support.

Pricing and ROI: Dremio is recognized for being cost-effective by reducing infrastructure complexities, making it competitive against options like Snowflake. Conversely, BigQuery's pay-as-you-go model can be budget-friendly if optimized but may lead to high costs with large data volumes. Both platforms provide valuable returns depending on usage, though pricing control and flexibility differ based on operational demands.

To learn more, read our detailed BigQuery vs. Dremio Report (Updated: March 2026).

BigQuery vs. Dremio

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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:

ROI

Sentiment score

4.9

BigQuery offers improved performance, cost savings, and intuitive features, with some users reporting up to 75% cost reductions.

Sentiment score

5.6

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

No quotes available

For more quotes and insights, download the BigQuery report

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

Customers find Google BigQuery support effective but sometimes limited, relying on documentation, forums, and expert assistance for issues.

Sentiment score

5.2

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

rating the customer support at ten points out of ten

Sr. Team Lead - IT at InfoStretch

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

For more quotes and insights, download the BigQuery report

Chief Technical Lead at a consultancy with 201-500 employees

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 offers excellent scalability and efficiency, supporting vast data seamlessly, though cost and integration challenges may arise.

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

We have not seen problems with scaling.

reviewer2807520

Director at a consultancy with 11-50 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.

For more quotes and insights, download the Dremio report

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 stable and reliable, with efficient data handling, few bugs, and strong support, despite occasional slow queries.

Sentiment score

7.2

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

In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.

reviewer2807520

Director at a consultancy with 11-50 employees

For more quotes and insights, download the BigQuery report

I rate Dremio a nine in terms of stability.

For more quotes and insights, download the Dremio report

SR BI developer at BRQ Digital Solutions

Room For Improvement

BigQuery struggles with cost, accessibility, scalability, and lacks data residency, needing better integration, performance, and machine learning features.

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

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

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

For more quotes and insights, download the BigQuery report

Chief Technical Lead at a consultancy with 201-500 employees

Starburst comes with around 50 connectors now.

KamleshPant

Senior Software Architect at USEReady

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

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

Setup Cost

BigQuery uses a pay-as-you-go model, balancing affordability with strategic expense management for data storage and query execution.

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

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Valuable Features

BigQuery provides scalable, serverless data processing with fast query capabilities, seamless GCP integration, SQL support, and competitive pricing.

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

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

Sr. Team Lead - IT at InfoStretch

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.

For more quotes and insights, download the BigQuery report

Chief Technical Lead at a consultancy with 201-500 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

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

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

6.9

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 March 2026, in the Cloud Data Warehouse category, the mindshare of BigQuery is 8.0%, up from 7.3% compared to the previous year. The mindshare of Dremio is 6.0%, down from 10.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	8.0%
Dremio	6.0%
Other	86.0%

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%

Manufacturing Company

14%

Computer Software Company

10%

Media Company

Financial Services Firm

26%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	13
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?

With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency. I cannot have e...

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, ...

What is your primary use case for Dremio?

I have been using Dremio for a year and a half. My main use case for Dremio is that I am able to access multiple databases and I can easily and quickly connect Dremio with my dashboards. In my rece...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Compared 16% of the time

Snowflake vs BigQuery

Compared 10% of the time

Teradata vs BigQuery

Compared 10% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 8% of the time

SAP Business Warehouse vs BigQuery

Compared 4% of the time

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Databricks vs Dremio

Compared 22% of the time

Snowflake vs Dremio

Compared 11% of the time

Starburst Enterprise vs Dremio

Compared 4% of the time

Dataiku vs Dremio

Compared 4% of the time

AtScale Adaptive Analytics (A3) vs Dremio

Compared 3% of the time

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Product Reports

BigQuery

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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

BigQuery vs. Dremio