AWS Lake Formation vs Dremio comparison

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AWS Lake Formation

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Executive SummaryUpdated on Feb 15, 2026

Dremio and AWS Lake Formation compete in the data management and governance space. Dremio appears to have an edge due to its robust data lineage capabilities and flexibility across deployment scenarios.

Features: Dremio offers robust data management capabilities, including integration with Apache Airflow and Amazon S3, and enables federated queries across multiple sources. It uniquely allows for the creation of visual tables without impacting original data. AWS Lake Formation is integrated within the AWS ecosystem, facilitating management of access patterns, data classification, and providing fine-grained access controls and centralized policies.

Room for Improvement: Dremio faces challenges with Delta connector support, long query execution times, and performance issues with specific databases. Its documentation requires enhancement. AWS Lake Formation could improve integration with non-AWS services and faces challenges with pricing and governance functionalities. Enhancements in real-time monitoring and cross-account sharing would be beneficial.

Ease of Deployment and Customer Service: Dremio provides flexibility with deployment options across public, hybrid, and on-premises environments. It receives positive feedback for professional services but faces challenges with staffing integration. AWS Lake Formation is primarily deployed in the AWS public cloud, offering centralized support, and is noted for its reliability but incurs higher costs for professional services.

Pricing and ROI: Dremio is perceived as cost-effective, reducing personnel needs and providing a competitive pricing model for enterprises managing data lakes. AWS Lake Formation's pricing is complex, tied to services like S3 and Athena, and varies based on consumption models, which might increase total costs.

To learn more, read our detailed AWS Lake Formation vs. Dremio Report (Updated: April 2026).

AWS Lake Formation vs. Dremio

April 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

AWS Lake Formation

Ranking in Cloud Data Warehouse

9th

Average Rating

8.0

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Dremio

Ranking in Cloud Data Warehouse

8th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Science Platforms (10th)

Mindshare comparison

As of May 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.1%, down from 5.3% compared to the previous year. The mindshare of Dremio is 5.1%, down from 10.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
Dremio	5.1%
AWS Lake Formation	4.1%
Other	90.8%

Cloud Data Warehouse

Featured Reviews

Ciro Baldim Guerra

Sr Analytics Engineer at Itau Unibanco S.A.

Has improved data governance by enabling clear ownership and structured access across teams

In my company, Itaú, we don't utilize all AWS offerings due to rigorous security measures. We operate approximately six to eight months behind other available services. I'm uncertain if gaps exist because of this limitation, though the system functions effectively for us. AWS Lake Formation offers column-level access control for databases, but we haven't implemented this feature either because it hasn't been approved by our compliance, governance, or security areas. In our current setup, everyone from my business unit uses the same consumer account. When access is requested for a table, everyone using that business unit account receives access. This could present a security concern, though it benefits new team members who automatically receive all necessary access permissions. However, I struggle to identify specific improvements needed in AWS Lake Formation.

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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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Quotes from Members

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

Pros

"A favorite feature of AWS Lake Formation is that it provides us with visibility into who has access to a particular table or database in Glue."

"We have observed measurable benefits in terms of cost savings, time savings, resource savings, and efficiency improvements in our workflows."

"The integration of AWS Lake Formation with the IAM for authentication and authorization is very good; I didn't have any problems in the setup and thought it was simple."

"The features and capabilities of AWS Lake Formation that I have found most valuable are that it is really convenient to see all the different data assets that were configured and understand who has and what type of service has or does not have access to those services."

"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."

"AWS Lake Formation significantly improves the structure of the data mesh, making it superior to previous structures we used."

"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."

"The LF-Tag system with granular permissions was key to the project as a functionality of AWS Lake Formation."

More AWS Lake Formation pros

"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."

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

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

"The native error interface has been most valuable."

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

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

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

"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."

More Dremio pros

Cons

"Despite following official documentation, configuration problems persisted, requiring weeks of support from multiple AWS engineers to resolve."

"You need to have data experience to use the product."

"Rather than creating an additional hundred tools, optimizing a tool to have a centralized location to do governance would be beneficial."

"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."

"If I could improve AWS Lake Formation, I would add more integrations with SageMaker."

"Athena can be a bit clunky when writing queries, indicating a potential enhancement point for easier user interaction with query tools such as DataGrip using provided driver JARs."

"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation."

"In our current setup, everyone from my business unit uses the same consumer account. When access is requested for a table, everyone using that business unit account receives access. This could present a security concern, though it benefits new team members who automatically receive all necessary access permissions."

More AWS Lake Formation cons

"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 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."

"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."

"The community version lacks information or documentation."

"We had a memory leak on some versions, which sometimes stopped the service."

More Dremio cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"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

Financial Services Firm

22%

Manufacturing Company

Retailer

Government

Financial Services Firm

26%

Computer Software Company

Healthcare Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	15

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	5
Large Enterprise	5

Questions from the Community

What is your experience regarding pricing and costs for AWS Lake Formation?

I don't understand much about the pricing of AWS Lake Formation, but I know how to search for the cost of Glue jobs, and I use the calculator in Amazon. I use a tool to preview the cost based on th...

What needs improvement with AWS Lake Formation?

Regarding areas of AWS Lake Formation that could be improved or enhanced, I prefer not to answer, mainly because I do not believe that I would be the most valuable person to ask, as I have not used...

What is your primary use case for AWS Lake Formation?

My usual use cases for AWS Lake Formation involved securing and governing the data resources that we configured in AWS, but we did not use the analytics or machine learning capabilities specificall...

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.

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, ...

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...

Snowflake vs AWS Lake Formation

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Compared 19% of the time

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Compared 11% of the time

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Compared 4% of the time

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Compared 4% of the time

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Product Reports

Download AWS Lake Formation product report

AWS Lake Formation

May 2026

Download Dremio product report

May 2026

Also Known As

No data available

Dremio AWS - BYOL

Overview

AWS Lake Formation streamlines data lake organization by integrating seamlessly with AWS services, ensuring robust access control, and facilitating effective data management. Its extensible framework and centralized data handling make it ideal for cloud environments that require scalability.

AWS Lake Formation offers comprehensive data synchronization with AWS, robust security through fine-grained policies, and seamless team collaboration tools. It centralizes data assets, supports analytics with Glue, and enhances data mesh structures. Its IAM integration supports efficient governance, with reliable data storage and retrieval backed by extensive documentation. While highly efficient within AWS ecosystems, Lake Formation faces integration challenges with third-party libraries, and users experience clutter in its interface. Aspects such as automation, policy management, and debugging are areas for improvement. Enhancements in orchestration, cross-account sharing, and integrations with services like SageMaker are user recommendations.

What are the key features of AWS Lake Formation?

Data Synchronization: Integrates with AWS services for seamless data management.
Security Policies: Implements fine-grained access control for secure data handling.
Team Collaboration: Facilitates cooperation through centralized data management.
Analytics Support: Enhances data processing with Glue integration.
Extensibility: IAM integration allows scalable governance.

What benefits or ROI can users expect?

Scalability: Efficiently manages expanding data volumes.
Cost Efficiency: Reduces cloud data management expenses.
Data Security: Ensures compliance with robust policies.
Comprehensive Support: Access to detailed documentation and AWS resources.

Organizations utilize AWS Lake Formation across various industries to build and manage data lakes, ensuring effective data governance and storage solutions. It helps integrate and transform data, control permissions, and support analytics. From e-commerce to healthcare, AWS Lake Formation delivers centralized data repositories for structured and unstructured data, securing information while providing scalable infrastructure and efficient resource management.

Amazon Web Services (AWS)

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.

Sample Customers

bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday

UBS, TransUnion, Quantium, Daimler, OVH