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.

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.

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.