Dremio Enterprise

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.

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.

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.

I have been using Dremio for approximately three years, since January of 2022.

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.

Dremio's scalability can handle growing data and user demands easily.

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.

Neutral

We haven't previously used a different solution before Dremio. Snowflake is the new solution we are currently using to replace Dremio.

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.

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

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.

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.

On-premises

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.

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.

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.

I have been working in my current field for five years.

In my experience, Dremio is reasonably stable.

I haven't pushed Dremio's scalability to its limit, so I cannot provide detailed information about that.

I haven't had the need to interact with Dremio's support team yet.

Positive

Before Dremio, we used Hive  and AWS Glue , and we wanted to shift to an open-source version that is newer than Hive and provides flexibility in moving between cloud and on-premises.

We evaluated other options before choosing Dremio, including Presto  and Trino, but we did not find reasonable advantages to using them.

I don't have the appropriate information regarding pricing, setup cost, and licensing as that is managed by a different team.

I cannot share return on investment information from using Dremio.

We evaluated other options before choosing Dremio, including Presto  and Trino, but we did not find reasonable advantages to using them.

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.

On-premises

Other

What do you like best about the product?

Its just how easy it is to use. When we first onboarded, I was surprised at how fast we could connect to, like, multiple data sources. Didn't have a huge setup headache, which was awesome.The implementation wasn't that bad, especially comparing to some other BI tools we used. I mean, it wasn't 100% smooth, had a few little hiccups, but overall we got it running way easier than I expected.
It's got pretty rich feature set—the reflections and acceleration stuff is cool for performance, even if it feels a bit overwhelming at the start. Integrating it with our existing stuff, like our AWS S3 buckets and Snowflake, was pretty straightforward. No major drama there,Oh, and the SQL editor is way better than I thought it'd be..Overall, it just feels like a tool built for speed and flexibility. we use sometimes multiple times a day when I have to do ad-hoc analysis or explore big datasets Yeah, there's definitely a learning curve, no lie. But once you get past that, you realize how powerful it is.

What do you dislike about the product?

Their customer support is decent. Sometimes they take a bit to get back to you, but most of the time I've gotten a proper solution that actually fixes the problem. The performance is weird sometimes, like one day a query runs blazing fast, and then the next day the exact same query is just... slower. For no obvious reason, The UI also feels a little clunky at times, not gonna lie. Especially when you're trying to handle a really large dataset, it'll just freeze up for a second , laggy . Makes the whole experience feel less smooth than it should.And the documentation... yeah, it could definitely be better. A lot of times I've had to just google around on forums or actually reach out to support just to find some small configuration detail that should really be in the main docs. Wastes a bunch of time.
Also it's not exactly cheap. When you start to really scale it up, especially running on our own cloud infra, the bills start to add up. I feel like for smaller teams, the admin side of things can feel too complex for what you need. Just setting up user permissions and everything is a whole thing.

What problems is the product solving and how is that benefiting you?

So Dremio's basically solved our whole issue with data being scattered everywhere. Before this, we were always having to copy and move data into some central system just to be able to run a query on it. Super time-consuming .We can now just query right on top of where the data lives. Like, directly on S3, or Snowflake, even some of our old legacy databases. We don't need to build these massive ETL pipelines just for a simple question, which is a game changer.It's also helped a ton with speed. Those reflections they have? They make a huge difference on heavy queries. Our reporting team used to have to wait like, hours for their results to come back, and now it's way faster. Saves a ton of times for our day-to-day analysis and helps us make decisions way quicker.

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