We use Dremio for data engineering.
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Dremio fast and reliable data lakehouse platform
What do you like best about the product?
Dremio is easy to configure and maintain, requiring little effort to implement from small to large clusters. It is well-documented, easy to extend, and integrates smoothly with other applications. It efficiently utilizes resources, though it has a powerfull C3 cache. The community site is highly active, providing strong support for questions about usage and configuration.
What do you dislike about the product?
Even though it is open source and part of the code is available on GitHub, they do not give importance to the community's efforts to collaborate with code modifications.
What problems is the product solving and how is that benefiting you?
With Dremio, we have solved the problem of efficiently storing data, consolidating multiple data sources, and having a robust lakehouse that allows us to process data quickly and deliver near real-time reports and dashboards.
A fast and easy SQL Engine on top a data lake file
What do you like best about the product?
It is quite easy to implement. Running on Kubernetes for full control. Integrate really well with our infrastructure. Dremio is running SQL coming from Power BI with ease. The UI is functionning quite well.
What do you dislike about the product?
The compagnie is still young. And quality of support took a bit of time to get up to par. But for now, we are very please with the product and the support.
What problems is the product solving and how is that benefiting you?
Provide a fast solution to run SQL on top of data lake file - Azure Storage Account - Parquet files.
Did a good job to solve this. And was fast to implement too.
Did a good job to solve this. And was fast to implement too.
Dremio Adoption experience
What do you like best about the product?
The product concept is straightforward and easy to explain.
Deploying/upgrading is simple especially with K8 deployments.
Open tech stack with Iceberg and Arrow is a big advantage
Deploying/upgrading is simple especially with K8 deployments.
Open tech stack with Iceberg and Arrow is a big advantage
What do you dislike about the product?
Learning curve is high. Teams need services help to succesfully deploy. More debug and self management will help in adopting and maintaining. This is critical for middleware technologies
Ease of integration has been a challenge expecially with the choice of BI Platform
Ease of integration has been a challenge expecially with the choice of BI Platform
What problems is the product solving and how is that benefiting you?
Abstraction to facilitate single source of truth. Ability to control and manage queries at scale is beneficial.
Modern Data Platform Analytics engine
What do you like best about the product?
Very fast. Supports Apache Iceberg as well as regular parquet file based tables. Supports large workload - we are running 500,000 queries reading 2PB of data daily. Arrow Flight is another great technology utilized by Dremio that increases end to end data pipeline peeformance.
What do you dislike about the product?
Dremio still has a few issues left that need to be polished out.
What problems is the product solving and how is that benefiting you?
Dremio processes large volume of data to deliver financial analytics to a broad range of our data platform consumes.
Fastest way to get iceburg working
What do you like best about the product?
You just need to give them your credit card
What do you dislike about the product?
Doesn't have all the ETL/ml features. Could have more feats
What problems is the product solving and how is that benefiting you?
It enforces acid transaction for our data lakehouse
Evaluating platform for data management and delivery
What do you like best about the product?
Apache Iceberg and Apache Arrow interoperability
What do you dislike about the product?
On-Premise Deployment and stability issues
What problems is the product solving and how is that benefiting you?
Delivering content and query capability to clients
Self Service for dummies
What do you like best about the product?
Users are in no time familiar with thr usage of Dremio, and administrative tasks in Dremio are super easy.
What do you dislike about the product?
AKS cluster is hard to maintain for us. Needs alignment with IT partner, but hard to find. Looking forward to use a Hosted Dremio solution.
What problems is the product solving and how is that benefiting you?
Connecting multiple sources and easy usage for end users.
A highly stable solution that works like a data warehouse on top of data lakes
What is our primary use case?
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
What do I think about the stability of the solution?
I rate Dremio ten out of ten for stability.
What do I think about the scalability of the solution?
I rate Dremio ten out of ten for scalability.
How was the initial setup?
I rate Dremio ten out of ten for the ease of its initial setup.
What about the implementation team?
We implemented the solution through an in-house team. Dremio's deployment can be done quickly.
What other advice do I have?
Overall, I rate Dremio ten out of ten.
Offers smooth installation and cloud/on-prem flexibility, but faces integration challenges with Databricks
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
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.
What do I think about the stability of the solution?
From my three months of experience, I haven't noticed any stability issues with Dremio.
What do I think about the scalability of the solution?
In my department, which focuses on data and AI, we have about 538 people. I'm not sure how many are actively using Dremio.
How was the initial setup?
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.
Which other solutions did I evaluate?
What other advice do I have?
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.
Which deployment model are you using for this solution?
Hybrid Cloud
Great Tool to bring many data sources of Big Data together with good performance
What do you like best about the product?
Its easy to configuring many different sources and create virtual data set that do unions across them. I am really happy about performance for data-retrieval
What do you dislike about the product?
There are so many bugs in each versions , So that we have to keep them updated with new release
What problems is the product solving and how is that benefiting you?
Dremio has a feature to make data accessible and usable by a much larger number of users . And reflections feature to make it so easy
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