Dremio Reviews & Product Details

-Convenient web interface for running SQL queries on our data

-Supports a broad range of data source

-Processing for queries is extremely fast when accelerated (rarely over 1 second)

-Python API is fairly easy to use

-Prompt and knowledgeable support team that are always willing to walk us through problems over video call Review collected by and hosted on G2.com.

It's hard to say, because most/all of the problems that we've had with Dremio are likely because we're several versions behind due to compatibility problems with some of our legacy infrastructure. eg. missing features that exist in newer versions, or instability problems with reflections that are probably remedied in newer versions. That's on us, though. Review collected by and hosted on G2.com.

Dremio managed to provide a wide range of existing analysis tools with universal, fast and direct access to the data in a data lake. The time required to provide data and the IT footprint is reduced to a minimum. Intelligent options for caching query results and entire aggregates in memory for specific analysis scenarios makes working with these two tools a pleasure. Review collected by and hosted on G2.com.

Currently there is no managed service on the Microsoft Azure platform, which makes deployment difficult. However, Dremio is working hard on this issue. Review collected by and hosted on G2.com.

Dremio is a powerful solution to connect your applications and BI tools to your data lake. They are very innovative and fast-paced to deliver new features and enhancements. The professional support team is very helpful and tries to understand the customer needs to deliver the best value. The variety of connectors available for the different data sources is really amazing as well the possibility to easily create custom connectors. The fact that the company contributes back to the community developing or open-sourcing tools is really nice. Review collected by and hosted on G2.com.

I believe Dremio's weakness is related to CI/CD because there is a couple of libraries, articles and even an open-sourced tool (which is great) to achieve it but it's still very hard and complex to have full automation. As I mentioned before, Dremio is very innovative and fast-paced, so, I believe they will address soon. One missing feature is the native support for Databricks Delta format but as far as I know it's on the roadmap and there is a workaround to be able to work with Delta. Another missing feature is the multi-master solution, it would be very helpful mainly when doing maintenances on the coordinator. Last but not least, it would be good to have the capability to use groups even when not integrated with an AD, LDAP, etc. Review collected by and hosted on G2.com.

Dremio helps us a lot to manage a high workloads from our reportnig systems and achieve a fast response time for more then 500 management dashboards. Many of our end-users like work with Dremio to avoid additional Data Engineering skills in team. For some of them it was surprisely fast after changing the Reporting from "Import" to "live" connection to move data processing directly to Dremio. But the most demanded feature was Reflections which gave sometimes lightning-fast (less then 1 second) response time without any re-engineering of business logic or reducing the data volumes.

In case of any issues and challenges Dremio was very cooperative on Germany and global level to solve it. Review collected by and hosted on G2.com.

As Dremio do not implemented Elastic Engine on Azure we need to maintain Kubernetes cluster to reach out needed ad-hoc scale-out requirements. Review collected by and hosted on G2.com.

* Integrates nicely with AWS. supports s3 buckets and aws hosted databases as data sources, as well as being able to use aws glue as a metastore.

* It's fast. Dremio is able to perform complex operations at scale very quickly. Many of our workloads that took tens of hours on our previous data analytics solution, now finish in under a minuet.

* Able to use a wide variety of data sources together. We able to seamlessly combine data from PostgreSQL and parquet files stored on S3 in a single query.

* Easy to connect to from external tools. Using their JDBC, a variety ODBC connectors and REST API we've been able to easily connect to, and use Dremio with a number of external tools on hosted linux or local windows. Jupyter, datagrip, excel, tableau.

* Great support and PS team. Having worked with the support team on issues ranging from inconsequential to major blockers, they have always been very responsive and fast acting. Review collected by and hosted on G2.com.

* Isn't currently transactional for data in an object store (s3). At least for an s3 data source you can't define a table then insert data into it. Any data written must be done via a Create table as Select style statement.

* Error clarity. initial errors displayed to users can be quite opaque requiring one to click through to a deeper menu to find the root cause. Review collected by and hosted on G2.com.

The product is great but for me its the people. Committed to our success, easy to work with, friendly and professional. From the beginning, we had positive interactions with Dremio and that didn't change after we became a customer. Dremio is a great partner for our company. Review collected by and hosted on G2.com.

Its still a relatively new platform so limited community information available. Review collected by and hosted on G2.com.

Fast and user-friendly query engine on top of open standard parquet files without the hassle of data loading process to a proprietary vendor format. We used to have to load our Spark-processed data to AWS Redshift in order to get decent performance from our datasets and then we use AWS Athena to avoid the hassle of secondary data loading, but encounter issues with performance SLA with Athena when traffic increases. With Dremio, we have the best of both worlds where the get the comparable performance of Redshift for most of our queries without the hassle of data loading and the reliable performance SLA. The nice user-friendly GUI that our users can use for their SQL queries is definitely a big plus for our end-user tooling and onboarding.

Aside from these, their AWS Edition has the great Elastic Engine feature that helps you save cost by turning off the engine when not in use and automatically turn it on when a query comes in. This has helped us keep our costs under control. Review collected by and hosted on G2.com.

The support for larger datasets with a large number of splits is an issue currently, but the move to use Apache Iceberg is in the works to overcome this limitation. Review collected by and hosted on G2.com.

The ease of use. We all know SQL and that is very flexible. No coding promises great things, but never deliver and complex development is taking a huge amount of time. Everybody understanding SQL should not go to No-Coding for speed, flexibility and (future) migration. Review collected by and hosted on G2.com.

The documentation on available functions is lacking. Dremio does not have a built-in Intellisense nor autosave. Review collected by and hosted on G2.com.

The confluence of open source technologies to solve one of the most challenging problems of todays Big Data environments. I applaud Dremio and its team in fusing together technologies like Apache Arrow, Vectorized engine, Iceberg etc to bring a unique approach to accelerating the data lake.

The approach to self service through semantic engineering of data has created a new dimension to data analysis and data curation. Review collected by and hosted on G2.com.

Dremio's lack of support for database views and external decryption libraries, has created a perception that sometimes overshadows all the advantages it brings. Review collected by and hosted on G2.com.

Simplification – Single point of data access.

Data Blending – Merge diverse data pools easily

Protection – Enable security and authorization.

Acceleration – Performant reporting and analysis Review collected by and hosted on G2.com.

Different roadmap AWS and Azure and not all capabilities you have in AWS are in Azure Review collected by and hosted on G2.com.

Dremio help us to transition from legacy to target state data lake architecture. it offers connectivity to both legacy data sources and modernised cloud native data lake on object storage. This allows us to produce insights across our whole data landscape since day 1 and plan out transition from legacy over time. Review collected by and hosted on G2.com.

Nothing major but it would be great if Dremio can offer connectors to more legacy data sources Review collected by and hosted on G2.com.

Enabling interactive speed queries on Hadoop Review collected by and hosted on G2.com.

Can be misused as a mainstream ELT tool. Review collected by and hosted on G2.com.

Upon selecting Dremio as a targeted consumption solution for our enterprise data footprint, Dremio immediately came along side our organization with their architects, designers, and client partners to understand the pending impediments. Through an arduous internal process, Dremio was able to quickly modify their platform to meet our security and compliance needs for a rapid deployment ultimately meeting out timelines. Review collected by and hosted on G2.com.

So far so good, they have been nothing but great partners. Review collected by and hosted on G2.com.

Amazing performance with Apache Arrow at its core with new enhancements actively developed with the open source community such as Gandiva for optimized execution and Flight for optimized data transfer. Project Nessie is also being worked on in the open source community to bring git-like version history to datasets. Review collected by and hosted on G2.com.

There can be better support for large datasets in the TB/PB range in terms of dataset management, with something like Iceberg support, which is in the works. Review collected by and hosted on G2.com.

What I like best about Dremio is the open source foundation and commitments to the open source community. This is most notable in the various database connector where we have the freedom to create our own or modify as needed. Review collected by and hosted on G2.com.

The limitations in connection to legacy DBs and there are limitation in maintaining sessions and passing things like "Alter" commands to source databases. This can increase development and adoption timelines. Review collected by and hosted on G2.com.

Easy to use, SQL based, No data movement, optimizing and integrate queries and/or the use of reflections for a high analyzing performance Review collected by and hosted on G2.com.

At this moment no actual issues, maybe a missing data dictionary Review collected by and hosted on G2.com.

Ability and ease to view data, especially parquet files and their data types. Review collected by and hosted on G2.com.

There is not much to dislike, the UI is great too and documentation is thorough. Review collected by and hosted on G2.com.

Dremio has a ton of support out of the box for a wide array of data sources. The professional services support is also world class. Review collected by and hosted on G2.com.

The scope of the amount of products it supports can lead to some slow problem resolutions for lesser used data source connections and edge case problems Review collected by and hosted on G2.com.

Dremio is a very simple tool to deploy and with OSS technology. We benched many solutions in order to find an performing tool that can accelerate Analytics & ML on-premise Review collected by and hosted on G2.com.

Need better integration with Analytics tools like spark (ex: native connectors...) Review collected by and hosted on G2.com.

able to connect data from multiple sources via Native SQL Review collected by and hosted on G2.com.

Sharding work on individual executors can be improvise to keep it simple Review collected by and hosted on G2.com.

Setup and query execution also awse features, reflections Review collected by and hosted on G2.com.

Sometimes machines crashes due to aws upgrades Review collected by and hosted on G2.com.