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: December 2024).

BigQuery vs. Dremio

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Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

5th

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

10th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Science Platforms (8th)

Mindshare comparison

As of December 2024, in the Cloud Data Warehouse category, the mindshare of BigQuery is 9.1%, up from 7.3% compared to the previous year. The mindshare of Dremio is 4.6%, up from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Sathishkumar Jayaprakash

Engineer at HTC Global Services (INDIA) Private

Efficient large dataset handling with seamless service integration

BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.

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MikeWalker

Founder/ CEO at Morphogen Data Inc.

It enables you to manage changes more effectively than any other platform.

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. There's another thing called data providence. They're tied together. Data providence allows you to go back and recreate the data at any particular point in time. It's extremely important for compliance and governance issues because data changes all time. How did it change? What was it three days or months ago? You may have made some decisions based on data that was three months old, so you might need to revisit those. It's essential for things like machine learning and deep learning, where you are generating AI models off data. When the model stops working or doesn't work as expected, you need to figure out why. You have to go back and adjust the datasets used to train the model. We do that through an open-source project called Nessie, which is their basis for providing data lineage and data province capabilities. It's super powerful. Arrow is another open-source project for storing data in memory and performing data query operations. Data sits on a disk in one format. If you want to do anything with data, you have to load it into your computer and put it into memory so you can work with it. Arrow provides a format in memory that enables the whole library to perform various operations on that data. Every vendor has its own way of representing data in memory. They've latched onto an industry standard and developed it so it's open. Now people can use the exact same format in memory to do operations and use the library set to perform functions on data. New developers can decide if they want to develop their own memory format or use one that's already there. Data transfer is a massive problem when you're working with large datasets, doing advanced analytics, and trying to train machine learning or deep learning models. What happens often is companies downsample their data sets to do training on models because transferring and managing data on a deep learning or machine learning platform is too much.

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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 product’s most valuable feature is its ability to manage the database on the cloud."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"The query tool is scalable and allows for petabytes of data."

"It has a well-structured suite of complimentary tools for data integration and so forth."

"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."

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

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

"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."

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 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."

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

"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."

"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."

Cons

"It would be helpful if they could provide some dashboards where you can easily view charts and information."

"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."

"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."

"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."

"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."

"The solution should reduce its pricing."

"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."

More BigQuery cons

"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."

"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."

"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."

"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."

"It shows errors sometimes."

Pricing and Cost Advice

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

"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."

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

"I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."

"BigQuery is inexpensive."

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

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

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

14%

Manufacturing Company

12%

Retailer

Financial Services Firm

32%

Computer Software Company

11%

Manufacturing Company

Retailer

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?

We are above the free threshold, so we are paying around 40 euros per month for BigQuery. It is generally low cost.

What needs improvement with BigQuery?

Sometimes, support specialists might not have enough experience or business understanding, which can be an issue. They might have basic knowledge but lack specific insights related to the specific ...

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?

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. We face certain issues when connectin...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Snowflake vs BigQuery

Compared 38% of the time

Teradata vs BigQuery

Compared 15% of the time

Vertica vs BigQuery

Compared 12% of the time

Compared 12% of the time

AWS Lake Formation vs BigQuery

Compared 4% of the time

More BigQuery Competitors

Databricks vs Dremio

Compared 48% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 8% of the time

Microsoft Power BI vs Dremio

Compared 4% of the time

Microsoft Azure Synapse Analytics vs Dremio

Compared 2% of the time

More Dremio Competitors

Product Reports

BigQuery

Download BigQuery product report

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Google

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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.

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