Sign in
Categories
Your Saved List Partners Sell in AWS Marketplace Amazon Web Services Home Help

Dremio Enterprise

Dremio | 23.0.1

Linux/Unix, Amazon Linux 2.0.20220912.1 - 64-bit Amazon Machine Image (AMI)

Reviews from AWS Marketplace

0 AWS reviews
  • 5 star
    0
  • 4 star
    0
  • 3 star
    0
  • 2 star
    0
  • 1 star
    0

External reviews

41 reviews
from G2

External reviews are not included in the AWS star rating for the product.


    santosh k.

User Friendly

  • November 24, 2022
  • Review provided by G2

What do you like best?
its easy to use and upgrades are simple compared to other products
What do you dislike?
Stability and time it takes for support to resolve issues
What problems is the product solving and how is that benefiting you?
Easy data access


    Computer Software

Dremio Review

  • December 14, 2021
  • Review provided by G2

What do you like best?
I like how easy it is to get the data ready w/o going through the ETL/ELT process.
What do you dislike?
They don't have their own BI too. You have to connect to the 3rd party tools.
What problems is the product solving and how is that benefiting you?
I tried to do a data analytic task.
I am pleased about how easy it is to perform my job.


    Verified User in Oil & Energy

Revolutionary technology with a natural user interface

  • October 27, 2021
  • Review verified by G2

What do you like best?
Dremio initially caught my eye because the company grew out of the open-source arrow project, which was already a fantastic project and critical to big data platforms.

Dremio does one thing really well and a couple of other things pretty well:
- To start with, with regards to scalable data access, whether you're accessing terabytes of parquet/files or megabytes of database information, Dremio _just works_. There are very few other solutions that 1) allow you to join different data sources on-demand, 2) do _not_ run 24/7 but spin up clusters when you need them and 3) have a reasonably user-friendly interface. The combination has made Dremio crucial to increasing productivity at my company.

However, Dremio offers even more than the killer feature of easy data access described above:
- Good mechanisms for data governance, including internal lineage graphs between datasets
- Ways to structure computing resources with regards to finetuning query performance -- if you need dashboard datasets to perform more quickly than UI datasets, it's almost a point-and-click operation
- You can expose internal statistics on usage and performance for all queries
- Better and better granularity with regards to managing users
- Most tools only allow users to download a max of 1-10k rows of data. Dremio easily allows 1 million rows and performs on this as well

Dremio has really thought about how companies should manage and expose data and has made sure to provide a design and the technology to make data access, democritization and governance easier.
What do you dislike?
Dremio is still a young company and while the product works well, they're still working very hard at improving it.

We have not yet run into a single bug on production, but it was initially noticeable that it's a young product (start 2021).

Fortunately, they are rapidly making new releases and fixing a lot of the little issues so that the product has a good, professional level of quality.
What problems is the product solving and how is that benefiting you?
As I mentioned above, Dremio solves data access in a performant, easy way -- and much easier, faster and in a smarter way than any other tool that I've yet to come across in 2021.
Recommendations to others considering the product:
Dremio is more expensive for smaller organizations -- even though we are smaller and it is thus more expensive for us, it's been able to solve problems that cheaper solutions were not capable of, in particular easy, scalable, manageable performant data access.


    Julien L.

Handy engine to bring many sources of Big Data together with good performance

  • July 21, 2021
  • Review verified by G2

What do you like best?
I really like the ease of configuring many different sources and create views that do unions across them. I'm also a big fan of Flight's performance for data-retrieval!
What do you dislike?
Dremio isn't an industry standard (yet), so help on the official forums or rest of the internet can be quite limited.
What problems is the product solving and how is that benefiting you?
We're aiming to offer a single view, that exposes near-live intraday data (in low-latency storage solutions) as well as far-back history (in high-bandwidth Big Data stores). We try to make it as easy, transparent and performant as possible for users to access any data we store internally.


    Daniel S.

Dremio as a next-generation data lake engine

  • June 21, 2021
  • Review verified by G2

What do you like best?
- Dremio allows business users to access data easily on multiple platform (HDFS, Oracle RDBMS, SQL Server, Json, etc.)

- Standardized semantic layer: Eliminate the need for copying and moving data—no more cubes, aggregation tables or extracts—and recurring instances of data drift.

- Accelerate dashboards and reports: Integrate BI tools such as Tableau, PowerBI directly with Dremio and accelerate dashboard/reporting queries

- Accelerate ad-hoc queries: Driving lightning-fast queries directly on your data lake storage. Dremio’s combination of technologies—including an Apache Arrow-based engine.
What do you dislike?
Lack of database connectors (a.e. BigQuery, Cassandra).

UI will be improved in the next version.
What problems is the product solving and how is that benefiting you?
We are putting in place a Data Virtualization Layer (Dremio) inside the company.
The business users are able to access and connect the data of multiple source systems with Dremio.
We are then aiming to launch Advanced Analytics use cases.


    Shayen Y.

Had a great experience building our Data Virtualisation layer with Dremio on top of our Data Lake.

  • June 10, 2021
  • Review verified by G2

What do you like best?
Lightning-fast query speeds, ability to easily work with data lakes
What do you dislike?
Intermittent issues, needs to be tested on a wider range of datasets
What problems is the product solving and how is that benefiting you?
Moving from Data Warehouse to Lakehoue
Recommendations to others considering the product:
Dremio is most suitable for organizations with huge volumes of data (in 100s of GBs). That is also when you will be able to see the value of Dremio vs a traditional Data Warehouse


    Lotar S.

Flexibility in data management

  • June 04, 2021
  • Review verified by G2

What do you like best?
-Dremio allows business analysts to access data easily on any platform (HDFS, Oracle RDBMS, EnterpriseDB, SQL Server, etc.)
-Cross-platform queries a huge advantage
-Helps to get rid of unneccesarry ETL jobs
-Brings data lake close to the business, it has helped a lot in the demistification of big data technologies
-There are continuous improvements which makes Dremio better and better
What do you dislike?
-Lack of NoSQL database connectors (HBase, BigQuery, Cassandra)
-The UI could be more user friendly
What problems is the product solving and how is that benefiting you?
Our objective was to consolidate and streamline data management across multiple data domains and provide fast access to customer data from diverse sources. With Dremio we have achieved it pretty fast.


    Computer Software

A great tool to unlock your data

  • June 01, 2021
  • Review provided by G2

What do you like best?
Dremio is simple to use, scales well and have great customer support
What do you dislike?
A larger community on the internet will help in resolving issues faster. Hopefully it will happen soon as more customers start using Dremio
What problems is the product solving and how is that benefiting you?
Unlocking the data in the data lake and the data warehouse


    Entertainment

Dremio is really cool!

  • June 01, 2021
  • Review verified by G2

What do you like best?
With a tiny engineering team (1) we were able to get Dremio up and running in AWS for our org to start using. It is extremely easy to bring silos of data from all over the organization held in various formats and make them available in our platform.

Once in the platform, it provides a non-threatening interface to allow both analysts and non-analysts the ability to search, find and query the data for their use cases. Dremio has done a wonderful job!
What do you dislike?
Dremio definitely puts the "democracy" in "data democratization", but I wish there were more tools to allow a little more control of what data sources are made public on the platform. An organization wouldn't want to be too strict over who can do things in this powerful platform, but being too open could result in data confusion.

Data governance tools to help make sure appropriate documentation or tagging are provided or possibily a request/approval workflow before something is made public to everyone would be really nice.
What problems is the product solving and how is that benefiting you?
The biggest problem we are solving is around the data silos in our organization. Dremio allows us to make data accessible and usable by a much larger population.
Recommendations to others considering the product:
If you are looking for a centralized data platform for your org, you should include Dremio in your evaluations. Definitely worth your time learning more about.


    Areg A.

Great software for data virtualization and Data Lake engine

  • May 26, 2021
  • Review verified by G2

What do you like best?
- Simple UI
- Low entry threshold for enduser (SQL is enough)
- Data Virtualization - one point to access lots of heterogeneous sources
- Materialization features
- Makes our ad hoc and self-service very fast.
What do you dislike?
- No scheduling for materializations yet.
- Some work is needed regarding logs/error descriptions - sometimes they're not very understandable.
What problems is the product solving and how is that benefiting you?
- Ad hoc analytics
- Data preparation for Data Science/AI/ML purposes
- Self-service reporting
- Centralizing access to various sources.
Recommendations to others considering the product:
Wisely consider your cluster size and your semantic layers structure