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: September 2026).

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BigQuery vs. Dremio

September 2026

Helped 920,769 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

4.2

BigQuery offers major cost savings and performance improvements, providing a strong ROI despite initial training costs and minimal documentation.

Sentiment score

4.0

Dremio offers cost savings and efficiency by reducing infrastructure needs and simplifying data source integration on data lakes.

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.

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SR BI developer at BRQ Digital Solutions

Customer Service

Sentiment score

6.8

BigQuery users find customer service varies, with premium users pleased but lower-tiered users facing access challenges and delays.

Sentiment score

6.1

Dremio's customer service is highly praised for responsiveness, with Gold plan users enjoying positive interactions despite some limitations.

rating the customer support at ten points out of ten

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Sr. Team Lead - IT at InfoStretch

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

Luís Silva

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

I would rate customer support a ten out of ten.

Director

We contact the technical support at least weekly; currently, it's more or less daily because we face performance issues, crashes of queries sometimes, and we also need support for configuration of the query engines and so on.

Baptiste Bergelin

Data Architect at a consultancy with 51-200 employees

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

7.6

BigQuery efficiently manages large datasets with cloud-based infrastructure and auto-scaling, despite occasional performance and cost concerns.

Sentiment score

7.3

Dremio's scalability excels, with user-configurable auto-scaling on Kubernetes/Docker, meeting large-scale needs, but some features require licensing.

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

Luís Silva

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.

JorgeADiaz

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

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

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

Dremio's scalability depends on your hardware and your deployment.

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Director

Stability Issues

Sentiment score

8.2

BigQuery is highly stable and reliable, efficiently handling large data with strong support, though improvements are suggested.

Sentiment score

7.2

Dremio users report stable performance overall, despite occasional memory leaks, scalability issues, and the need for frequent monitoring.

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

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I rate Dremio a nine in terms of stability.

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SR BI developer at BRQ Digital Solutions

We refactored the query, and that worked, but it still doesn't make you confident to go to production with a product that might crash depending on the query you send it.

Baptiste Bergelin

Data Architect at a consultancy with 51-200 employees

Room For Improvement

BigQuery users want better user-friendliness, integrations, cost efficiency, advanced AI features, and simplified data handling for large datasets.

Dremio users face issues with performance, connectors, learning curve, and desire better integration and schema flexibility.

Improvement is needed in the sense that the AI model behind BigQuery should understand sometimes what the enterprise architecture is and how efficiently we can write queries.

Jitender Singh_

Senior Manager - Consulting & Alliances at a outsourcing company with 51-200 employees

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

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Sr. Team Lead - IT at InfoStretch

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

JorgeADiaz

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

Before we wrap up, I want to add that Dremio is lacking a semantic layer in a manner that the user can just choose dimensions, metrics, and filters to query the data.

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Director

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.

reviewer2769915

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

Setup Cost

BigQuery's competitive pricing offers cost flexibility, but query costs can rise without proper optimization, making planning essential.

Dremio offers cost-effectiveness but licensing is costly, leading some users to prefer the community version for scalability.

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

Luís Silva

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.

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Sr. Team Lead - IT at InfoStretch

I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.

Burak Uğur

Data Engineer at a tech services company with 201-500 employees

I had a good experience with pricing, as the pricing seems okay to me and the client.

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Director

Dremio does have a core of the engine that is open-source, but all the important features for the enterprise version are under license.

Baptiste Bergelin

Data Architect at a consultancy with 51-200 employees

Valuable Features

BigQuery offers a scalable, cost-effective solution with AI, machine learning, fast response, and seamless tool integration for data analysis.

Dremio excels in data virtualization, federated queries, and integration, enhancing data management and access with efficient tools.

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

JorgeADiaz

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

We were able to save 60% of our time that used to go into writing SQL queries.

Jitender Singh_

Senior Manager - Consulting & Alliances at a outsourcing company with 51-200 employees

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

For more quotes and insights, download the BigQuery report

Sr. Team Lead - IT at InfoStretch

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

reviewer2769915

Data Consultant 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

Multiple data sources can be queried in one query, and it is not just a query engine.

Burak Uğur

Data Engineer at a tech services company with 201-500 employees

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Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

2nd

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Dremio

Ranking in Cloud Data Warehouse

10th

Average Rating

8.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Science Platforms (9th)

Mindshare comparison

As of November 2026, in the Cloud Data Warehouse category, the mindshare of BigQuery is 6.9%, down from 8.0% compared to the previous year. The mindshare of Dremio is 4.3%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	6.9%
Dremio	4.3%
Other	88.8%

Cloud Data Warehouse

Featured Reviews

reviewer2807520

Director at a consultancy with 11-50 employees

Data processing has become faster and large analytical queries run reliably at multi‑region scale

The features I have found most valuable in BigQuery include the query cache, which is good and scales well. We have not seen problems with scaling. It handles huge amounts of data quite well, and the multi-regions feature works well. To measure the effectiveness of BigQuery, I think it is quite good. The metrics I use to evaluate BigQuery's effectiveness include that scaling is quite good and handling of big amounts of data. We do not see any glitches, and we do not see major timeouts. Google platform in general has been quite stable, and we do not see that many outages at resource level or at overall zonal level.

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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

14%

Outsourcing Company

14%

Manufacturing Company

10%

Media Company

10%

Financial Services Firm

22%

Outsourcing Company

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	15
Midsize Enterprise	10
Large Enterprise	21

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	6
Large Enterprise	5

Questions from the Community

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?

The queries that BigQuery provides are not optimized at a greater level. For simple tasks it is good, but if I want to write complex analyzing queries, it doesn't give the queries up to the mark, s...

What is your primary use case for BigQuery?

My main use case for BigQuery is the analysis of our data, creating dashboards from large datasets, and querying the databases that we have, including both structured and unstructured data imported...

What is your experience regarding pricing and costs for Dremio?

I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.

What needs improvement with Dremio?

I think Dremio can be improved to query faster than multiple sources and big data lakehouses. The community version could be updated for new data catalog connections. I think the documentation is s...

What is your primary use case for Dremio?

I have been using Dremio to select multiple data sources, retrieve multiple data sources, and create and maintain Iceberg tables for the data lakehouse. My main use case for Dremio is to actively c...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Snowflake vs BigQuery

Compared 11% of the time

Compared 8% of the time

Teradata vs BigQuery

Compared 8% of the time

Snowflake Analytics vs BigQuery

Compared 7% of the time

IBM Db2 Warehouse vs BigQuery

Compared 5% of the time

More BigQuery Competitors

Databricks vs Dremio

Compared 14% of the time

Snowflake vs Dremio

Compared 12% of the time

Starburst Enterprise vs Dremio

Compared 3% of the time

Dataiku vs Dremio

Compared 3% of the time

Amazon SageMaker vs Dremio

Compared 3% of the time

More Dremio Competitors

Product Reports

Download BigQuery product report

BigQuery

October 2026

Download Dremio product report

October 2026

Also Known As

Dremio AWS - BYOL

Overview

BigQuery is a powerful cloud-based data warehouse offering advanced SQL querying, seamless Google integration, and scalable handling of large datasets. Its serverless architecture and built-in AI capabilities facilitate efficient data processing and insights extraction.

BigQuery provides an efficient data analysis platform with low-latency performance and cost-effective on-demand pricing. Leveraging Google's cloud infrastructure for data storage, it offers robust security and high availability. While it excels in SQL support and caching features, it can improve on user accessibility, integration with diverse tools, and machine learning feature expansion. Making it more accessible for smaller entities through improved cost management and local data compliance is essential. Enhancements in query speed and intuitive interfaces can further optimize performance.

What features are offered by BigQuery?

User-friendly interface: Simplifies data interaction, making complex processes more manageable.
Serverless architecture: Eliminates the need for infrastructure management, improving scalability and efficiency.
Advanced SQL querying: Enables complex data retrieval and manipulation with ease.
Machine learning integration: Facilitates AI-driven insights directly from the platform.
Cost-effective pricing: On-demand pricing helps manage expenses by paying only for used resources.
Seamless Google integration: Works well with familiar Google services for a cohesive experience.
High availability and robust security: Ensures data is secure and accessible when needed.

What benefits should be considered when evaluating BigQuery?

Efficient data processing: Quickly handles large datasets, supporting fast data-driven decision-making.
Scalable infrastructure: Grows capacity alongside business needs without manual infrastructure adjustments.
Enhanced data analysis: SQL support and caching features improve analysis precision and speed.
AI and machine learning capabilities: Enable advanced data insights and predictive analytics.
Integration with existing tools: Ensures smooth workflow transitions with services such as Google Analytics and Tableau.

In industries like healthcare, finance, and marketing, BigQuery is extensively used for data storage, generating reports, and supporting ETL processes. Educational institutions leverage it for analytics, aligning seamlessly with Google Cloud for serverless infrastructure efficiencies.

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