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: July 2025).

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:

Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.2

Reviews Sentiment

7.2

Number of Reviews

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

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 6.3%, down from 8.3% compared to the previous year. The mindshare of Dremio is 10.5%, up from 6.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Luís Silva

Chief Technical Lead at Human Code

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."

"The initial setup process is easy."

"BigQuery has a very nice interface that you can easily learn if you know SQL."

"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."

"The interface is what I find particularly valuable."

"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."

"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."

"It's straightforward to set up."

More BigQuery pros

"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."

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

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

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

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

"Overall, you can rate it as eight out of ten."

"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."

More Dremio pros

Cons

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

"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

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

"Sometimes, support specialists might not have enough experience or business understanding, which can be an issue."

"The product’s performance could be much faster."

"The process of migrating from Datastore to BigQuery should be improved."

"We would like to be able to calibrate the solution to run on top of a raw file."

More BigQuery cons

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

"It shows errors sometimes."

"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."

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

"They need to have multiple connectors."

"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."

"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."

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

More Dremio cons

Pricing and Cost Advice

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

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

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

"BigQuery is inexpensive."

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

"Its cost structure operates on a pay-as-you-go model."

"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."

"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."

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

16%

Financial Services Firm

15%

Manufacturing Company

11%

Retailer

Financial Services Firm

30%

Computer Software Company

Manufacturing Company

Healthcare Company

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...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Compared 18% of the time

Snowflake vs BigQuery

Compared 16% of the time

OpenText Analytics Database (Vertica) vs BigQuery

Compared 15% of the time

Teradata vs BigQuery

Compared 14% of the time

SAP Business Warehouse vs BigQuery

Compared 3% of the time

More BigQuery Competitors

Databricks vs Dremio

Compared 43% of the time

Snowflake vs Dremio

Compared 16% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Dataiku vs Dremio

Compared 5% of the time

Amazon Redshift vs Dremio

Compared 3% of the time

More Dremio Competitors

Product Reports

BigQuery

Download BigQuery product report

Download Dremio product report

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

BigQuery vs. Dremio