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: April 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

4th

Average Rating

8.2

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Dremio

Ranking in Cloud Data Warehouse

9th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (9th)

Mindshare comparison

As of May 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.0%, down from 8.2% compared to the previous year. The mindshare of Dremio is 11.0%, up from 5.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

VikashKumar1

Tech lead at Ford Motor Company

Easy to maintain and provides high availability

Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process. Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process. After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.

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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

"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."

"Even non-coders can review the data in BigQuery."

"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."

"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."

"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."

"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."

"The initial setup is straightforward."

"BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data."

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."

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

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

"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."

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

"Everyone uses Dremio in my company; some use it only for the analytics function."

"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."

More Dremio pros

Cons

"When I execute a query, the dashboard doesn't always present the output seamlessly."

"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"So our challenge in Yemen is convincing many people to go to cloud services."

"We'd like to see more local data residency."

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

"They could enhance the platform's user accessibility."

"The main challenges are in the areas of performance and cost optimizations."

More BigQuery cons

"It shows errors sometimes."

"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."

"They need to have multiple connectors."

"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."

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

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

"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 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."

More Dremio cons

Pricing and Cost Advice

"The solution's pricing is cheaper compared to other solutions."

"The price is a bit high but the technology is worth it."

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

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"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."

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

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

More BigQuery pricing and cost advice

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"Dremio is less costly competitively to Snowflake or any other tool."

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Top Industries

By visitors reading reviews

Computer Software Company

17%

Financial Services Firm

15%

Manufacturing Company

11%

Retailer

Financial Services Firm

31%

Computer Software Company

10%

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?

The price is perceived as expensive, rated at eight out of ten in terms of costliness. Still, it offers significant cost savings.

What needs improvement with BigQuery?

When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming. In general, if I know SQL and start playing around, it will star...

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

Snowflake vs BigQuery

Compared 21% of the time

Compared 17% of the time

Teradata vs BigQuery

Compared 16% of the time

Vertica vs BigQuery

Compared 15% of the time

SAP Business Warehouse vs BigQuery

Compared 2% of the time

More BigQuery Competitors

Databricks vs Dremio

Compared 49% of the time

Snowflake vs Dremio

Compared 16% of the time

Starburst Enterprise vs Dremio

Compared 5% of the time

Microsoft Power BI vs Dremio

Compared 5% of the time

KNIME vs Dremio

Compared 2% 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