Dremio Enterprise

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.

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.

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.

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

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.

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.

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.

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.

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.

I have used it for just one month for proof of concept purposes.

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.

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.

It was just proof of concept, and we were just exploring the product. We did not deal with technical support 

Neutral

It is a SaaS, so it is straightforward to set up.

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.

Public Cloud

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