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

Dremio Enterprise

Dremio | 21.7.0

Linux/Unix, Amazon Linux 2.0.20221103.3 - 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.


    Information Technology and Services

Data Virtualization

  • November 30, 2020
  • Review provided by G2

What do you like best?
able to connect data from multiple sources via Native SQL
What do you dislike?
Sharding work on individual executors can be improvise to keep it simple
What problems is the product solving and how is that benefiting you?
- Central Data virtualization
- Democratizing Data
- Technical edge


    Financial Services

Strategic Partner

  • November 26, 2020
  • Review provided by G2

What do you like best?
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.
What do you dislike?
So far so good, they have been nothing but great partners.
What problems is the product solving and how is that benefiting you?
In our large scale data environment we are looking to leverage strategic tools like Dremio in order to limit the transformation and ingestion activities that currently take place within our enterprise lakes. Dremio will ultimately reduce our ETL developments allowing us to deliver insights and results to our business partners significantly faster.


    Financial Services

It is really a very fast analytic engine compare to other competitors

  • November 25, 2020
  • Review provided by G2

What do you like best?
Setup and query execution also awse features, reflections
What do you dislike?
Sometimes machines crashes due to aws upgrades
What problems is the product solving and how is that benefiting you?
Query execution is too fast around 7 times faster than athena


    Automotive

Self service Data lake acceleration

  • November 24, 2020
  • Review provided by G2

What do you like best?
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.
What do you dislike?
Dremio's lack of support for database views and external decryption libraries, has created a perception that sometimes overshadows all the advantages it brings.
What problems is the product solving and how is that benefiting you?
Democratizing data by getting data into the hands of the users through self service. Reaping the benefits of Arrow for speed. Leveraging Dremio's support for Iceberg to cater to consumption scenarios that are unpredictable. Leveraging Iceberg's hidden partitioning through Dremio is a key in this area.


    Andrej S.

My Dremio experience as enterprise-wide data platform by big German client

  • November 24, 2020
  • Review verified by G2

What do you like best?
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.
What do you dislike?
As Dremio do not implemented Elastic Engine on Azure we need to maintain Kubernetes cluster to reach out needed ad-hoc scale-out requirements.
What problems is the product solving and how is that benefiting you?
We have a different use cases on the same shared Dremio instance - "classical" Management Reporting, Self Service BI, Data exploration, AI Use Cases and Business Process Automation.
So our worloads ware not equal in time and not always predictable from "big-bang" requests and high-volume scans. But Dremio managed it in smooth way.
Recommendations to others considering the product:
Based on my exprerience Dremio fits for usecases when you:
..have Multi-Cloud stategy and want to avoid "lock-in" effect into one of cloud-vendor solution
..have onPremise Hadoop cluster or ODS store which performance is not enough
..have end users which wants to work directly with data, but have only SQL knowlegde
..want to offload data processing to Dremio from you BI-tools like Tableau or Power BI
..have usecases where time-to-market has a huge value (like a ad-hoc data exploration in Data Science)


    Logistics and Supply Chain

Next Gen tool to accelerate Datalake & AI

  • November 23, 2020
  • Review provided by G2

What do you like best?
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
What do you dislike?
Need better integration with Analytics tools like spark (ex: native connectors...)
What problems is the product solving and how is that benefiting you?
- Accelerate Analytics
- Avoid duplicated datasources shared between Analytics & ML
- Self-service DataHub for business
Recommendations to others considering the product:
Give a try to Dremio ! you will be surprised.


    Financial Services

Efficient and User Friendly SQL layer on top of open file format

  • November 20, 2020
  • Review verified by G2

What do you like best?
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.
What do you dislike?
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.
What problems is the product solving and how is that benefiting you?
Data virtualization and democratization. As most of our users come from SQL background, Dremio is the perfect solution for these users to something that they are already familiar with. The web GUI has streamlined our data quality analysts' job as they can simply perform their initial data exploration from the GUI.
Recommendations to others considering the product:
If you dislike proprietary vendor format and the hassle of the data loading process, try this out. Try their community AWS Edition which has most of the features that you would need.


    Financial Services

Solid platform and excellent support.

  • November 19, 2020
  • Review provided by G2

What do you like best?
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.
What do you dislike?
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.
What problems is the product solving and how is that benefiting you?
Dremio is positioned to solve for virtualization and federation to legacy databases as well as their core product of enabling SQL interfaces on data lakes.


    Financial Services

Dremio as a Data Wrangling Tool

  • November 19, 2020
  • Review provided by G2

What do you like best?
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.
What do you dislike?
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
What problems is the product solving and how is that benefiting you?
A unified query layer to allow data exploration among disparate data sources
Recommendations to others considering the product:
Their docs are extremely thorough.


    Financial Services

Fast and Open Data Lake Engine

  • November 18, 2020
  • Review provided by G2

What do you like best?
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.
What do you dislike?
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.
What problems is the product solving and how is that benefiting you?
Building an open file-based data warehouse that can scale to speed up existing RDBMS-based workloads and allow new and more advanced use cases to be developed on top of data from various sources.