I use Dremio for proof of concept purposes. I haven't used it in a real-time project, however, I explore Dremio as a data virtualization application in the ecosystem. It is relatively new, possibly a one-year or two-year-old system.
Dremio Enterprise
Dremio | 25.2.15Linux/Unix, Amazon Linux 2.0.20250623.0 - 64-bit Amazon Machine Image (AMI)
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Unified lakehouse platform for Analytics and Al
What do you like best about the product?
I love the platform’s ability to connect to a wide array of data sources, including relational databases (PostgreSQL, MySQL, Oracle, MS SQL), NoSQL systems (MongoDB, Elasticsearch), and cloud or file-based storage like S3 and HDFS, without requiring complex ETL pipelines.
This approach simplifies data integration and reduces engineering overhead.
The SQL query engine is highly performant, delivering sub-second response times even on large datasets, and supports live data visualization and dynamic previews during query preparation.
Data reflections feature acts as an intelligent caching layer, optimizing query performance and enabling low-latency dashboard refreshes for BI workloads.
The platform’s virtual datasets allow for complex query logic to be encapsulated and reused, supporting data-as-code principles such as Git-like version control and experimentation.
Cloud-native architecture offers elastic compute scaling and is available as a managed service on AWS and Azure, making it suitable for both on-premises and cloud deployments. It supports role-based access control and multitenancy, which is essential for enterprise environments with strong data governance requirements.
This approach simplifies data integration and reduces engineering overhead.
The SQL query engine is highly performant, delivering sub-second response times even on large datasets, and supports live data visualization and dynamic previews during query preparation.
Data reflections feature acts as an intelligent caching layer, optimizing query performance and enabling low-latency dashboard refreshes for BI workloads.
The platform’s virtual datasets allow for complex query logic to be encapsulated and reused, supporting data-as-code principles such as Git-like version control and experimentation.
Cloud-native architecture offers elastic compute scaling and is available as a managed service on AWS and Azure, making it suitable for both on-premises and cloud deployments. It supports role-based access control and multitenancy, which is essential for enterprise environments with strong data governance requirements.
What do you dislike about the product?
The learning curve can be significant, especially when configuring advanced features like data reflections, multitenancy, and integrating with complex enterprise authentication systems.
While the UI is functional, some administrative and monitoring functions feel less intuitive compared to other modern analytics platforms.
I have also found that fine-grained access controls and tenant isolation require careful configuration to avoid inadvertent data exposure in multi-tenant scenarios.
While the UI is functional, some administrative and monitoring functions feel less intuitive compared to other modern analytics platforms.
I have also found that fine-grained access controls and tenant isolation require careful configuration to avoid inadvertent data exposure in multi-tenant scenarios.
What problems is the product solving and how is that benefiting you?
Dremio has eliminated the need for traditional ETL pipelines in my analytics workflows, allowing direct querying and data exploration across disparate sources without data movement.
This has resulted in faster dataset creation cycles and reduced bottlenecks between data engineering and analytics teams.
The platform’s autonomous performance optimization and use of data reflections have significantly improved query speeds, enabling real-time analytics and interactive BI dashboarding even on large, complex datasets.
By adopting Dremio, I achieved unified access to both structured and semi-structured data in a single platform, which streamlined data governance and cataloging.
The self-service model empowered business analysts to experiment and iterate on data products without constant engineering intervention, accelerating time-to-insight for AI and analytics projects.
The platform’s open, standards-based approach has also made it easier to integrate with existing tools and future-proof my data infrastructure against vendor lock-in concerns.
✅ My overall insight: Dremio has enabled a more agile, scalable, and cost-effective analytics environment, supporting both operational BI and advanced data science initiatives in a unified, governed, and performant manner.
This has resulted in faster dataset creation cycles and reduced bottlenecks between data engineering and analytics teams.
The platform’s autonomous performance optimization and use of data reflections have significantly improved query speeds, enabling real-time analytics and interactive BI dashboarding even on large, complex datasets.
By adopting Dremio, I achieved unified access to both structured and semi-structured data in a single platform, which streamlined data governance and cataloging.
The self-service model empowered business analysts to experiment and iterate on data products without constant engineering intervention, accelerating time-to-insight for AI and analytics projects.
The platform’s open, standards-based approach has also made it easier to integrate with existing tools and future-proof my data infrastructure against vendor lock-in concerns.
✅ My overall insight: Dremio has enabled a more agile, scalable, and cost-effective analytics environment, supporting both operational BI and advanced data science initiatives in a unified, governed, and performant manner.
Easy Direct Access
What do you like best about the product?
I like the fact that you can query directly s3 and hdfs and it also support power bi as integration
What do you dislike about the product?
Its not etl friendly so I have to link it with apache ariflow
What problems is the product solving and how is that benefiting you?
The easy integration so i save time
Review for Dremio product
What do you like best about the product?
its great experience using Dremio. I have used its sql query engine product. the implementation was very easy and good for freshers and non-tech people. it's not too expensive w.r.t to other platforms. I like the speed. it's quite fast. I like the customer support service.
What do you dislike about the product?
there is nothing which I dont like as I like it and its good to try on different platform for cloud and analytics work.
What problems is the product solving and how is that benefiting you?
I have used its sql query engine product. the implementation was very easy and good for freshers and non-tech people.
its very helpful for data analytics and visulizations.
its very helpful for data analytics and visulizations.
Work
What do you like best about the product?
It is super user friendly and helps to handle big ranges of data.
What do you dislike about the product?
Fairly speaking there is not a lot to say about that. Users can get what they expect from optimal data cloud.
What problems is the product solving and how is that benefiting you?
It helped a lot to me to combine different sources and process them together easily and faster.
Solution offers quick data connection with an edge in computation
What is our primary use case?
What is most valuable?
It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want.
They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.
What needs improvement?
They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst does. Starburst has all these capabilities. Dremio has only 15 to 20 connectors, however, Starburst comes with around 50 now.
For how long have I used the solution?
I have used it for just one month for proof of concept purposes.
What do I think about the stability of the solution?
I cannot comment on stability as I just worked with it for one month. I haven't worked with large data. When I worked with small data, it was fine at that time.
What do I think about the scalability of the solution?
Internally, if it's on Docker or Kubernetes, scalability will be built into the system. In the SaaS, I'm unsure as I haven't set it up. I don't know how the integrated SaaS works inside. If it were an enterprise setup like Starburst, I know how it works since I have worked there, using Kubernetes, Docker, and everything. I'm not familiar with Dremio's backend, however, it also works on Kubernetes and similar technologies. Hopefully, scalability will be there for sure.
How are customer service and support?
It was just proof of concept, and we were just exploring the product. We did not deal with technical support
How would you rate customer service and support?
Neutral
How was the initial setup?
It is a SaaS, so it is straightforward to set up.
What other advice do I have?
Regarding features, I'm not sure if they have all the tools like data governance, data quality, and data lineage integrated. If not, they need to build those tools as well to check the data quality and lineage. Data discovery is there. Connectivity-wise, Starburst is way better, however, Dremio might have a better computing path, possibly delivering data faster than Starburst. No direct comparison can be made, so I cannot comment further.
Overall, you can rate it as eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Effortless data analytics with flexible integration and room for advanced schema capabilities
What is our primary use case?
We use Dremio for financial data analytics and as a data lake. We connect Dremio with Oracle, Docker, MySQL, and utilize it for Power BI.
Additionally, we use it to process data from MongoDB, although we face occasional challenges with NoSQL integration.
What is most valuable?
Dremio is very easy to use for building queries. It's easy to connect Dremio to various databases and data sources like Oracle and MySQL. It is also very flexible, providing us with scalability and integration capabilities effortlessly.
What needs improvement?
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 connecting Dremio to MongoDB, especially with max values, which seem to be inconsistent in Dremio.
Additionally, licensing is quite expensive, and we feel the need for more flexible schema capabilities, especially in embedding JSON from MongoDB.
For how long have I used the solution?
We have been using Dremio for more than two years.
What do I think about the stability of the solution?
In terms of stability, we experience performance issues occasionally, partly due to our limited experience.
What do I think about the scalability of the solution?
We have not yet scaled Dremio because we are using the community version, and require a license for scaling. We need to learn how to scale up and out more effectively.
How are customer service and support?
We haven't submitted any questions for technical support yet.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We have worked with various data warehouse solutions, like Oracle, and also used databases on Azure.
How was the initial setup?
The initial setup was straightforward. We run Dremio on an Ubuntu dedicated server on-premises, and it took us about a day to set up.
What was our ROI?
We see savings because we don't need more personnel to develop and maintain Dremio. It's cost-effective in terms of manpower.
What's my experience with pricing, setup cost, and licensing?
The licensing is very expensive. We need a license to scale as we are currently using the community version.
Which other solutions did I evaluate?
We used Oracle and databases on Azure before using Dremio.
What other advice do I have?
Dremio is very flexible and easy to use, making it very suitable for our team. I would recommend Dremio for similar use cases due to its flexibility.
On a scale of one to ten, I would rate Dremio at eight.
Which deployment model are you using for this solution?
On-premises
Dremio is an excellent semantic layer and caching/acceleration/extract layer
What do you like best about the product?
The Reflections feature is awesome. It allows you to seamlessly accelerate queries by pre-aggregating query results at various aggregations that you can define over time, based on actual query usage. If you move away from Tableau, Dremio is how you can replace Tableau's Extract feature. Or, if you're using Superset, you can insert Dremio in between Superset and the data sources to auto-magically speed up slow queries and dashboards.
What do you dislike about the product?
Dremio OSS does not support encrypted ODBC or Flight connections. You need Enterprise for that.
What problems is the product solving and how is that benefiting you?
Speeding up slow Superset dashboards.
Good enterprise version bad open source
What do you like best about the product?
Easily centralizing data, with many sources and resources
What do you dislike about the product?
High difficult to mantain the environment, high resources demands
What problems is the product solving and how is that benefiting you?
Centralizing data
Data Engineer with 2 years experiences with Dremio
What do you like best about the product?
Virtualization data , also the open sources like Arrow, Iceberg.
What do you dislike about the product?
sometimes I had the OOM error and dont' know exactly why
What problems is the product solving and how is that benefiting you?
Data virtualization. that helps us a lot because at RTE, we have many different databases
Dream big with Dremio for big data processing
What do you like best about the product?
Dremio is a game changing tools that enhance our consultation service company to provide client with all the forecast, data management, and data transformation that client require. Easy tools to operate is the main attraction and visualized analytics report tools help client understand data that we present. Running K8 deployment/setup is easy with the help from detailed documentation. Apache Hadoop integration simplify data distribution workflow.
What do you dislike about the product?
Bugs on every version that they releases. After one bugs is fixed on update, other is emerged and need to wait for hotfix or update. The problem keep cycles.
What problems is the product solving and how is that benefiting you?
Simple solution for data analytics workflow and operation. Data integrity tools that remove and eliminate data duplication.
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