
Overview
Dremio delivers lightning-fast queries and a self-service semantic layer directly on S3, so that you don't have to move the data into data warehouses, cubes or extracts.
Highlights
- Fast queries on S3 (4-100x faster & 10x more efficient than other SQL engines)
- Join between S3 and other AWS/on-premise databases
- Semantic layer to empower BI (Tableau, Power BI, etc.) users and govern data access
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Buyer guide

Financing for AWS Marketplace purchases
Pricing
Dimension | Cost/hour |
|---|---|
m5d.2xlarge | $0.78 |
m5d.xlarge | $0.39 |
m5d.8xlarge | $3.12 |
m5d.4xlarge | $1.56 |
i3.4xlarge | $2.15 |
r5d.4xlarge | $1.99 |
c5d.18xlarge | $5.96 |
Vendor refund policy
No refunds
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Dremio Deployment
Launches a coordinator node of the product, with the ability to dynamically provision additional engines to execute queries
CloudFormation Template (CFT)
AWS CloudFormation templates are JSON or YAML-formatted text files that simplify provisioning and management on AWS. The templates describe the service or application architecture you want to deploy, and AWS CloudFormation uses those templates to provision and configure the required services (such as Amazon EC2 instances or Amazon RDS DB instances). The deployed application and associated resources are called a "stack."
Version release notes
Additional details
Usage instructions
Quickstart Instructions:
Resources
Vendor resources
Support
Vendor support
Community Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.


Standard contract
Customer reviews
Evaluating Dremio for Enterprise Data Analytics
The user interface is clean and intuitive. It is web-based, which means no client installations to manage, and the layout is straightforward. The SQL editor is responsive with autocomplete and syntax highlighting, making query writing feel natural. The visual query builder is helpful for users who are not comfortable with SQL, though most of our team prefers writing queries directly. The dashboarding and visualization features are functional but not as polished as dedicated BI tools like Tableau or Power BI. For quick exploratory analysis, they work perfectly, but for polished executive presentations, we still export to other tools. Overall, the user experience is pleasant and focused on getting work done without unnecessary friction.
The integrations are where Dremio truly excels. The platform connects to over 36 different data sources, including all the major databases, data warehouses, and cloud storage platforms. We have it connected to PostgreSQL for transactional data, Snowflake for our warehouse, and S3 for our data lake files. The integration process is straightforward. You add a source, provide credentials, and the system catalogs the metadata automatically. No complex configuration required. The federated query engine means we can join data across these sources in a single query, which was impossible for us before. The platform also integrates well with BI tools like Tableau and Power BI through standard JDBC and ODBC connectors, allowing our analysts to work in the tools they already know.
The performance gains are genuinely impressive. When we first set up Dremio, we ran a test query that used to take about 8 minutes in our legacy environment. Dremio returned it in about 12 seconds. I actually thought it was broken. The Autonomous Reflections feature is magical. It learns what queries we run most frequently and automatically creates optimized data structures in the background. We do not have to think about performance tuning anymore. It just happens. Even with complex joins across multiple data sources, the query engine handles it efficiently. We have seen query times improve by 10x to 100x depending on the complexity of the workload.
The cost savings are another major win. We were paying a fortune for cloud data warehouse storage because we were duplicating everything. We had data in S3, data in Snowflake, data in Redshift, and we were paying for all of it. Dremio let us store everything in open formats on S3 and query it directly. We cut our storage costs by about 60 percent and our compute costs by about 40 percent. The open architecture is a big part of this. We are not locked into any proprietary format. If we decide to move away from Dremio, our data is still there in standard formats like Apache Iceberg. No vendor lock-in, no expensive migration project. The pricing model is consumption-based, so we only pay for what we use, which aligns well with our variable workload patterns. For organizations with significant data volumes, the ROI is substantial and measurable within the first year.
The onboarding experience was remarkably smooth compared to other enterprise platforms we have implemented. Dremio offers a Community Edition that we used to prototype and test before committing to the enterprise version. This allowed us to validate the platform's capabilities without any financial risk. The enterprise version includes formal support, and we have found the support team to be responsive and knowledgeable. Tickets are resolved quickly, and the documentation is thorough and well-organized. The platform is self-managed in our environment, which gives us full control, but Dremio also offers a fully managed cloud option if you prefer not to handle the infrastructure. The open-source community is active and helpful, which is a nice supplement to the formal support channels. Overall, getting started was straightforward and we were productive within weeks rather than months.
The AI Agent is a nice addition. It handles basic natural language questions well. A user can ask "Show me revenue by region for the last quarter" and it will generate the SQL and visualize the results instantly. It saves time on simple queries and helps non-technical users get started. The AI_GENERATE function is particularly interesting. It can extract structured information from unstructured files like PDFs or text documents directly within a SQL query, which opens up new use cases for us. The AI Semantic Layer ensures that both human analysts and AI tools are working with consistent business definitions, which improves the accuracy of AI-generated answers. However, the AI capabilities are not yet at the level where they can replace a skilled analyst for complex analytical questions. For day-to-day exploration and quick insights, it is genuinely helpful, but complex multi-condition analysis still requires human expertise.
Overall, Dremio has made us more agile, more cost-effective, and more data-driven as an organization. The reduction in complexity has been liberating. We are spending less time moving data and more time actually analyzing it. I would recommend it to any organization struggling with data silos, high costs, or slow access to insights. It is not perfect, but for what it does, it is remarkably effective.
Integrations are a particular sore spot. The connection with Power BI has been described as problematic by multiple reviewers, with customer support teams apparently having a poor understanding of how this integration works internally. This makes troubleshooting incredibly difficult when something goes wrong. The CI/CD capabilities also feel incomplete. There is an unsupported Python script available to handle development and production environments, but it cannot deal with some native Dremio objects like Row-Level Security policies. This means you might develop a dataset with proper access controls in your development environment, but the script simply will not deploy it to production, forcing you to find workarounds.
Performance at enterprise scale has been a documented weak point. The federation engine struggles with concurrency on large datasets, and standardized performance testing shows that the architecture routinely fails to complete queries when multiple users are running queries simultaneously. For AI use cases, this is more pronounced because an AI agent querying your data may be running dozens of complex federated queries in parallel as part of a single task, and the failure mode compounds quickly.
There are also technical limitations to be aware of. The platform enforces various system limits, such as maximums on autoingest pipes, sources, spaces, and total reflections. Query results returned via the console are limited to one million records, though you can use ODBC or JDBC to see the full result set. The platform also has limits on leaf columns in tables and queries, which can impact organizations working with wide datasets.
Support quality has been inconsistent. Some users have noted that support can be lacking at times, which they attribute to Dremio being a younger company compared to its competitors. The documentation around certain features, like Kubernetes deployment requirements, indicates significant administrative overhead, and managing the Kubernetes cluster requires considerable effort.
The AI capabilities, while promising, are somewhat lagging compared to other platforms. In a world where competitors have already integrated the latest AI techniques, Dremio is still catching up. The AI Semantic Layer, while useful for labeling data, does not enforce business rules. It organizes and labels data without packaging metadata, access rules, or business constraints into an enforceable framework. Different teams and AI agents can still interpret the same underlying data in incompatible ways, and the platform has no mechanism to stop them.
There is also a broader strategic concern. Dremio was recently acquired by SAP, which puts its roadmap in question. Features that Dremio was building independently now have to compete for budget and attention inside a much larger organization. Strategic shifts are already visible, with some cloud editions effectively put on hold while the deal works through approvals. For organizations with strict data governance requirements, the mandatory transmission of operational telemetry data back to Dremio's corporate endpoint introduces a compliance headache that cannot be ignored.
Despite these challenges, I still find Dremio valuable for our use case. The query performance is genuinely impressive, the cost savings are real, and the platform has transformed how we access data. The downsides are real and you need to go in with your eyes open. Budget extra time for troubleshooting integrations, be prepared for some administrative overhead, and have realistic expectations about the AI capabilities. If you have a focused use case like fast SQL acceleration on an Iceberg lakehouse where you own all the data, Dremio's core engine is still remarkably capable. But for teams needing broad federation across many sources, high concurrency, and polished enterprise features, it is worth evaluating carefully.
The most significant business problem Dremio solved for us is eliminating the traditional ETL bottleneck. Instead of building complex pipelines to extract, transform, and load data into a central warehouse, we now query data directly where it lives. This "query, don't move" approach has dramatically accelerated our time to insight. When we need to combine data from our transactional database with our data lake, we simply write a query and Dremio handles the federation. What used to take days of pipeline development now takes minutes of query writing.
We were also struggling with data silos that made it nearly impossible to get a unified view of our business. Different teams had different versions of the truth, leading to endless debates about whose numbers were correct. Dremio connects to all our data sources and presents them through a unified semantic layer, creating a single source of truth for the entire organization. Our finance team can now confidently use the same data as operations, and we no longer waste time reconciling conflicting reports.
The cost savings have been substantial as well. We were paying a premium to store duplicated data across multiple cloud warehouses. Dremio's open architecture, built on Apache Iceberg, allows us to store data once in open formats on object storage and query it efficiently without duplication. We eliminated redundant storage costs and reduced our cloud compute spending because we are not constantly moving and transforming data.
Perhaps most importantly, Dremio has enabled self-service analytics across our organization. Previously, every data request had to go through a centralized IT team that was perpetually backlogged. Business users waited weeks or months for access to the data they needed. Now, our analysts can query data independently using standard SQL, without waiting for engineering support. The platform has democratized access to data, and we have seen adoption grow as more teams realize they can explore data on their own terms.
Dremio has fundamentally shifted our organization from being reactive to proactive with data. We can now answer business questions in hours instead of weeks, spot trends earlier, and make decisions based on current information rather than stale reports. The platform has given us the agility we needed to compete in a data-driven world, and I would not want to go back to our old way of working.
Flexible SQL for Handling Data from Many Sources
Has improved access to diverse databases and accelerated dashboard integration
What is our primary use case?
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 recent project, my databases are full of information about our insurance operation, and I monitor my KPIs by connecting Dremio to my dashboards. I have multiple KPIs that I have to stay tuned to, and I need to access that very quickly and in an easy way, which is how Dremio helps me.
About my use case with Dremio, I find databases that I don't know very much about, and in that web environment of Dremio, I am able to scroll and navigate through the databases by searching for an example or a column name, so it is much easier to navigate through with that kind of help.
What is most valuable?
The best features Dremio offers, in my opinion, probably include the web environment.
What stands out to me about the web environment is both the interface and the speed; it helps a lot.
In terms of features, querying is very easy and the integration with multiple dashboards is amazing.
Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.
A specific outcome that shows how Dremio has helped my organization is definitely better data access.
What needs improvement?
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, 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.
For how long have I used the solution?
I have been working with data analysis for five years now.
What do I think about the stability of the solution?
In my experience, Dremio is stable.
What do I think about the scalability of the solution?
I have never had any experience scaling up or down with Dremio.
How are customer service and support?
I have never tried to reach customer support.
Which solution did I use previously and why did I switch?
I didn't get to choose the solution I used before Dremio, which was SQL Server from Microsoft. Nowadays I see that SQL Server solution is much more simple, and Dremio helps me a lot to move beyond that simplicity.
What other advice do I have?
My advice for others looking into using Dremio is to explore the web environment and stay sharp with the ODBC connectors and drivers, as that is very important. I would rate this product a 9.
Has simplified complex data integration workflows and supported consistent reporting across multiple sources
What is our primary use case?
As a BI developer, my main use case for Dremio involves registering data zones and also doing inquiries, views, and making it persistent to use on BI tools, like Tableau reports. In a project, we create data zones, typically one that we have in our company to receive files. After doing the ingestion inside Dremio , what we do is set up permissions, and after setting up the permissions, the refresh session allows us to go over and start building the views and all those things to be possible to connect via JDBC driver and consume on Dremio dashboard.
What is most valuable?
The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift. We build a SQL on Dremio standard, and then Dremio goes over and rewrites the whole lineage to make it possible to use multiple sources of data.
The feature impacts our daily work by reducing complexity because we don't need to worry about where the data comes from. We are slightly migrating to Snowflake , so Dremio is currently being replaced because Snowflake is a more robust platform, but we are kind of happy with the work Dremio does.
I think the reduction of complexity is a positive impact that Dremio has had on my organization. The main thing is that it is a place where you can write down simple SQLs and see the lineage, the way it integrates together; this is the best part of it.
What needs improvement?
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.
For how long have I used the solution?
I have been using Dremio for approximately three years, since January of 2022.
What do I think about the stability of the solution?
We had a memory leak on some versions, which sometimes stopped the service. Many times, we need to stop and restart the service to clear all the cache and all that. I rate Dremio a nine in terms of stability. I think it is stable, but we need to restart it many times, and we need to monitor it regularly.
What do I think about the scalability of the solution?
Dremio's scalability can handle growing data and user demands easily.
How are customer service and support?
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues. Many times it's with vendors where there is a missing feature or ongoing problem that they say will be fixed in the next release, but this happens not only with Dremio but with almost every vendor we have.
Which solution did I use previously and why did I switch?
We haven't previously used a different solution before Dremio. Snowflake is the new solution we are currently using to replace Dremio.
What was our ROI?
I cannot share the exact metric itself, but Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate. Dremio was a solution that made it possible for us to have different data sources with only one registration on our data lake.
What's my experience with pricing, setup cost, and licensing?
I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.
Which other solutions did I evaluate?
I probably don't remember all the options we evaluated, but we talked a lot about Databricks and Redshift, and Dremio was the best choice. Also, we don't use Tableau Prep because we use Dremio, which is an important thing.
What other advice do I have?
My advice to others looking into using Dremio is that it is a great tool because it keeps all your efforts together. It's a good thing to have if you want to have a unified catalog or metadata or something like this, so this is certainly a good tool to consider. I would rate this review an eight out of ten overall.
Has created a unified workspace for data teams and reduced storage costs through centralized access
What is our primary use case?
I have been using Dremio on and off as a data warehouse for the past three years.
My main use case for Dremio is that we use it as a logical data warehouse where we use Dremio with VDSs as an alternative to AWS Glue or Apache Hive . As we are working with our ETL at the end of all of it, after the data types and everything have been cast, we make that available on Dremio as VDS and then we move on to our further data warehousing schemes within Dremio.
We use Dremio enterprise-wide now, and the key use case has been reducing our costs when it comes to data storage.
Our main use case for Dremio is as a data warehouse, and the challenge that it helped us solve is that physical data warehouses such as Redshift have storage and hardware upscaling conflicts. Dremio helps us decouple those and lets us catalog more. We can manage everything under one system.
What is most valuable?
The best features Dremio offers include having a single system where we can manage all of our data cataloging and visualization or virtualization.
The interface is a plus over the traditional warehousing solutions, which makes it easier to work with Dremio compared to other solutions I've used.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
Dremio has positively impacted my organization by helping us create a single source of truth, a singular data warehouse where we can have access to all of the data sets. The fact that Dremio has a clear role-based access management system helps us significantly, as we can have roles segregating all of the data, while users with the appropriate roles can access everything.
What needs improvement?
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. With the Dremio software version that we're using, all that requires a learning curve, and only when you go to the premium cloud version do you get Dremio Arctic. It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
For how long have I used the solution?
I have been working in my current field for five years.
What do I think about the stability of the solution?
In my experience, Dremio is reasonably stable.
What do I think about the scalability of the solution?
I haven't pushed Dremio's scalability to its limit, so I cannot provide detailed information about that.
How are customer service and support?
I haven't had the need to interact with Dremio's support team yet.
Which solution did I use previously and why did I switch?
How was the initial setup?
We evaluated other options before choosing Dremio, including Presto and Trino, but we did not find reasonable advantages to using them.
What about the implementation team?
I don't have the appropriate information regarding pricing, setup cost, and licensing as that is managed by a different team.
What was our ROI?
I cannot share return on investment information from using Dremio.
Which other solutions did I evaluate?
We evaluated other options before choosing Dremio, including Presto and Trino, but we did not find reasonable advantages to using them.
What other advice do I have?
I would advise others looking into using Dremio to study the tool beforehand. Dremio has several different offerings, and the best way to get into it is to use the open-source on-premises version to experiment with it. To extract the complete power of the platform, organizations should educate themselves with the complete information and compare it with other solutions since a single solution cannot fit everywhere. Educating the team before adopting a technology is better than just adopting a suggested package. I rate Dremio 8 out of 10.