BigQuery vs Dremio comparison

Read 8 Dremio reviews

2,959 Views
2,229 Comparison Views

100% willing to recommend

BigQuery

Comparison Buyer's Guide

Download the report

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).

Download the complete report

BigQuery vs. Dremio

April 2025

Helped 851,371 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:

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.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."

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

"The feature called calibrating the capacity is valuable."

"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."

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

"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."

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

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

More BigQuery pros

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

"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."

"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."

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

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

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

"Dremio allows querying the files I have on my block storage or object storage."

More Dremio pros

Cons

"BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses."

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

"When I execute a query, the dashboard doesn't always present the output seamlessly. Troubleshooting requires opening each pipeline individually, which is time-consuming."

"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."

"The product could benefit from improvements in user-friendliness, particularly in terms of the user interface."

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

"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."

"It would be beneficial if BigQuery could be made more affordable."

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."

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

"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."

"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."

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

More Dremio cons

Pricing and Cost Advice

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

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

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

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

"The tool has competitive pricing."

"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."

"BigQuery is inexpensive."

"The platform is inexpensive."

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."

See which vendors are best for you

Use our free recommendation engine to learn which Cloud Data Warehouse solutions are best for your needs.

See recommendations

851,371 professionals have used our research since 2012.

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 20% of the time

Compared 16% of the time

Teradata vs BigQuery

Compared 16% of the time

Vertica vs BigQuery

Compared 14% 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

Download BigQuery product report

BigQuery

May 2025

Download Dremio product report

May 2025

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