AWS Lake Formation vs Dremio comparison

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AWS Lake Formation

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Executive SummaryUpdated on Nov 9, 2025

Dremio and AWS Lake Formation compete in data management and analytics solutions. Dremio seems to have the upper hand in terms of advanced analytics capabilities through its federated query and integration support, while AWS Lake Formation excels in comprehensive data governance and security.

Features: Dremio offers compatibility with various data storage solutions, advanced analytics support with Apache Airflow and Arrow, and visual data structures for enhanced monitoring and compliance. AWS Lake Formation provides integrated data governance, tag-based access, and centralized access management for seamless connection with AWS services.

Room for Improvement: Dremio lacks Delta connector support and faces challenges with large queries, SQL compatibility, and integrations with platforms like Databricks. AWS Lake Formation struggles with third-party application integration and is costly while needing improvements in comprehensive access control and advanced auditing capabilities.

Ease of Deployment and Customer Service: Dremio supports both on-premises and cloud deployments but may face accessibility challenges due to staffing limitations. AWS Lake Formation smoothly integrates within AWS, though adjustments may be needed for hybrid setups, and its intricate components can be challenging to deploy for those without technical expertise.

Pricing and ROI: Dremio is seen as cost-effective with some licensing concerns, benefiting greatly from reduced maintenance needs. AWS Lake Formation has complex pricing due to AWS service integration, considered expensive but valuable when fully utilized with AWS. Both require a careful cost-benefit analysis relative to organizational use and integration.

To learn more, read our detailed AWS Lake Formation vs. Dremio Report (Updated: December 2025).

AWS Lake Formation vs. Dremio

December 2025

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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

7th

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

5th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Science Platforms (11th)

Mindshare comparison

As of January 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 5.0%, up from 5.0% compared to the previous year. The mindshare of Dremio is 6.6%, down from 9.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
Dremio	6.6%
AWS Lake Formation	5.0%
Other	88.4%

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

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

"There is no doubt that this place exceeded my expectations with its incredible ambiance, attentive service, and mouthwatering menu."

"I can easily move data from cold storage to regular storage."

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

"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."

"The most valuable features of AWS Lake Formation were the access model itself, as it allows implementation of filters, Blueprints, and row-level and column-level security to mask data that shouldn't be accessed by certain entities, enabling granular control without exposing PII data."

"The solution has many features that are applicable to events such as audits."

"In the shortest form, what I appreciated about AWS Lake Formation was that the schema definition and data cataloging were quite good."

More AWS Lake Formation pros

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

"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."

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

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

"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."

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

More Dremio pros

Cons

"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."

"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."

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

"I would appreciate online support, which I don't have access to in my corporation at the bank, so that is important."

"It falls short when it comes to more granular access control, such as cell-level or row-level entitlements which is a significant drawback for organizations that require precise control over who can access specific rows of data."

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

"For the end-users, it's not as user-friendly as it could be."

"The initial onboarding process is challenging because creating a plan takes a month to a month and a half to build out."

More AWS Lake Formation cons

"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."

"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."

"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."

"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 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."

More Dremio cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

Manufacturing Company

Retailer

Financial Services Firm

28%

Computer Software Company

Manufacturing Company

Healthcare Company

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 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?

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, ...

Snowflake vs AWS Lake Formation

Comparisons

Compared 18% of the time

Azure Data Factory vs AWS Lake Formation

Compared 18% of the time

Amazon Redshift vs AWS Lake Formation

Compared 15% of the time

Databricks vs AWS Lake Formation

Compared 15% of the time

BigQuery vs AWS Lake Formation

Compared 10% of the time

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Compared 33% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Dataiku vs Dremio

Compared 6% of the time

Amazon SageMaker vs Dremio

Compared 5% of the time

More Dremio Competitors

Product Reports

Download AWS Lake Formation product report

AWS Lake Formation

January 2026

Download Dremio product report

January 2026

Also Known As

No data available

Dremio AWS - BYOL

Overview

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.

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