Dremio vs Google Cloud Datalab comparison

Dremio and Google are both solutions in the Data Science Platforms category. Dremio is ranked #10 with an average rating of 8.5, while Google is ranked #22. Dremio holds a 2.1% mindshare in Data Science Platforms, compared to Google’s 1.7% mindshare. Additionally, 100% of Dremio users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Dremio

Read 11 Dremio reviews

4,589 Views
2,386 Comparison Views

100% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

2,400 Views
1,416 Comparison Views

80% willing to recommend

Dremio

Google Cloud Datalab

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Executive SummaryUpdated on Feb 15, 2026

Google Cloud Datalab and Dremio are competing products in the data management and analytics space. Dremio generally has the advantage due to its advanced features and data performance, making it favorable for maximizing data potential, whereas Google Cloud Datalab is appreciated for its integration capabilities.

Features: Google Cloud Datalab offers seamless integration with Google Cloud services, facilitating collaboration and visualization in data science workflows. It provides a reliable infrastructure and simplifies data visualization. Dremio excels with its data as a service platform, offering advanced data virtualization, self-service, and acceleration features. It offers the capability to create virtual datasets and supports various data storage systems, providing a single interface for data management.

Room for Improvement: Google Cloud Datalab could enhance the configuration of its AI features to make node transitions smoother and streamline its dashboard automation. Dremio could benefit from reducing the complexity of managing data lineage and improve its support for seamless integration with new data sources, potentially by enhancing SaaS options. Additionally, improving documentation and user support could help new users adapt quickly.

Ease of Deployment and Customer Service: Google Cloud Datalab offers a streamlined deployment within the Google ecosystem, supported by extensive online resources and community support. Dremio provides a flexible deployment model that fits multiple environments with comprehensive documentation to facilitate use. The primary difference is the ease of integration offered by Google Cloud Datalab versus the flexibility of Dremio.

Pricing and ROI: Google Cloud Datalab typically involves lower upfront costs, benefiting from scalable pricing models appealing to budget-conscious organizations. In contrast, Dremio might involve higher initial setup costs but provides greater long-term ROI due to its robust data features. This pricing difference highlights Google Cloud Datalab's cost-effectiveness against Dremio's capability-driven ROI.

To learn more, read our detailed Dremio vs. Google Cloud Datalab Report (Updated: August 2026).

Buyer's Guide

Dremio vs. Google Cloud Datalab

August 2026

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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

Dremio

Ranking in Data Science Platforms

10th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th)

Google Cloud Datalab

Ranking in Data Science Platforms

22nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (26th)

Mindshare comparison

As of August 2026, in the Data Science Platforms category, the mindshare of Dremio is 2.1%, down from 3.5% compared to the previous year. The mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Dremio	2.1%
Google Cloud Datalab	1.7%
Other	96.2%

Data Science Platforms

Featured Reviews

Corrr Moray

SR BI developer at BRQ Digital Solutions

Has simplified complex data integration workflows and supported consistent reporting across multiple sources

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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LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

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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

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

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

"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."

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

"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."

More Dremio pros

"The APIs are valuable."

"All of the features of this product are quite good."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"Google Cloud Datalab is very customizable."

"For me, it has been a stable product."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

Cons

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

"They need to have multiple connectors."

"Dremio could be improved by making it easier for data cataloging, especially when working with open table formats, as you have to choose a data format and then go into it."

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

"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."

"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."

"Many developers and programmers, when they are starting to work in this specific field or area, are much more used to SQL Server, the Microsoft way of querying, and Dremio has some features that are different when we are talking about the syntax of coding, so I would improve that."

"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."

More Dremio cons

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"The product must be made more user-friendly."

"The interface should be more user-friendly."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

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."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"It is affordable for us because we have a limited number of users."

"The product is cheap."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Outsourcing Company

Manufacturing Company

Construction Company

20%

Financial Services Firm

13%

Outsourcing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	5
Large Enterprise	5

No data available

Questions from the Community

What is your experience regarding pricing and costs for Dremio?

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

See all answers

What needs improvement with Dremio?

I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...

See all answers

What is your primary use case for Dremio?

I have been using Dremio for a year and a half. My main use case for Dremio is that I am able to access multiple databases and I can easily and quickly connect Dremio with my dashboards. In my rece...

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Comparisons

Databricks vs Dremio

Compared 16% of the time

Snowflake vs Dremio

Compared 13% of the time

BigQuery vs Dremio

Compared 5% of the time

Amazon SageMaker vs Dremio

Compared 4% of the time

Dataiku vs Dremio

Compared 4% of the time

More Dremio Competitors

Tableau Enterprise vs Google Cloud Datalab

Compared 8% of the time

Databricks vs Google Cloud Datalab

Compared 8% of the time

Qlik Sense vs Google Cloud Datalab

Compared 7% of the time

Dataiku vs Google Cloud Datalab

Compared 7% of the time

Anaconda Business vs Google Cloud Datalab

Compared 6% of the time

More Google Cloud Datalab Competitors

Product Reports

Buyer's Guide

Dremio

August 2026

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Buyer's Guide

Google Cloud Datalab

August 2026

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Also Known As

Dremio AWS - BYOL

No data available

Overview

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.

Dremio

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Sample Customers

UBS, TransUnion, Quantium, Daimler, OVH

Information Not Available

Buyer's Guide

Dremio vs. Google Cloud Datalab

August 2026

Free Report: Dremio vs. Google Cloud Datalab

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