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.

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.

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.

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.

We have been using Dremio for more than two years.

In terms of stability, we experience performance issues occasionally, partly due to our limited experience.

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.

We haven't submitted any questions for technical support yet.

Positive

We have worked with various data warehouse solutions, like Oracle, and also used databases on Azure.

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.

We see savings because we don't need more personnel to develop and maintain Dremio. It's cost-effective in terms of manpower.

The licensing is very expensive. We need a license to scale as we are currently using the community version.

We used Oracle and databases on Azure before using Dremio.

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.

We use Dremio for data engineering.

Dremio has resolved my data lineage and data governance problems. The solution has also resolved the data availability for a different range of users, which used to be a problem.

Dremio allows querying the files I have on my block storage or object storage. The solution gives me a place where I can play around with the data virtually by creating VDSs or PDSs. Dremio works just like a data warehouse on top of my data lake, which is interesting.

Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported.

The use case I am working on requires building trees and hierarchical structures. Most of the time, it requires complex nested data structures to be made simpler for end users. It would be good if Dremio could provide a way to create trees just like Oracle does using commands like CONNECT BY and NO CYCLE.

You can use a few languages to simplify complicated JSON and XML. It would be very helpful if Dremio could provide a solution to simplify building trees and building meaningful data from complex data.

I rate Dremio ten out of ten for stability.

I rate Dremio ten out of ten for scalability.

I rate Dremio ten out of ten for the ease of its initial setup.

We implemented the solution through an in-house team. Dremio's deployment can be done quickly.

Overall, I rate Dremio ten out of ten.

We have been using it to build one of our frameworks. We primarily use Dremio to create a data framework and a data queue. It's being used in combination with DBT and Databricks.

We're still in the exploration phase with Dremio, so it's a bit early to determine its most valuable feature. We're currently deploying it across different departments for various use cases and learning from these internal applications.

We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily. We had to set up two different VMs and execute them in a different manner and integrate them. 

I've been using Dremio for about two to three months now. However, one of our teams has been using it for the past year.

From my three months of experience, I haven't noticed any stability issues with Dremio.

In my department, which focuses on data and AI, we have about 538 people. I'm not sure how many are actively using Dremio.

The installation process was quite smooth and didn't present any issues.

We currently have Dremio on the cloud. For proof of concept (POC) purposes, we are using it on-premises.

We are currently evaluating Dremio against other similar products. But at first glance, I would recommend using Dremio.

Considering my limited access and experience over these three months, I would rate Dremio around a seven out of ten.

I can visualize traffic from BI and Tableau on the same page and have my tables and schema on the same page. The data link comprises everything. If I want one structure, I connect it to a big table in the hive and the data team that could read my SQL work on my tables, schemas, table structures and everything all in one place. Dermio is as good as any other Presto engine.

Everyone uses Dremio in my company; some use it only for the analytics function.

The most valuable feature is that you can generate refresh reflections and create your visuals (VDS) because it makes it easier to monitor day-to-day data structure. I can use Dermio to create a visual table without impacting the original by creating an opportunity on my own. I can work on my videos and create reflections, and Dremio allows me to use those reflections. I can know the health of my tables, whether healthy or unhealthy, daily. It is one of the most valuable features of Dremio.

One of the areas of improvement is that a table does not break and shows errors. When the 23rd version was released, I had to contact Dermio customer support, and they suggested I update the database to run the table. Sometimes you face common errors like tables running strictly and taking time to run. I think Dermio can improve this part.

An additional feature can be a feature where everyone can see the tables.

I have been using Dremio for over 3years now.

I would rate the scalability an 9/10 because it shows errors sometimes.

I would rate the scalability a  10/10 based on its recent enhancements.

We had Dremio support service at every point of time we requested it.

Positive

Previously, I used Presto regularly.

The initial setup was straightforward.

The deployment was in-house, and only two to three people were involved. We had direct Dermio support communication to support us. We have both cloud and in-house deployment methods.

With Dremio its not a loss

Every tool has a value based on its intended purpose and use, and the pricing is worth its value.

We tested some other options but Dremio proofs to be better in terms of reliability and scalability, so we submitted our reports and reviews.

I suggest that you give it a try and see for yourself. Dremio is as fast as Presto. For example, ten billion rows can return in less than five seconds.

I would rate it a 10 out of 10.