Dremio AWS - Community Edition
Dremio | 24.3.3Linux/Unix, Amazon Linux 2.0.20240131.0 - 64-bit Amazon Machine Image (AMI)
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With Dremio and Power BI combined, a data-driven culture can be established, on all data. (Gemany)
What do you like best about the product?
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.
What do you dislike about the product?
Currently there is no managed service on the Microsoft Azure platform, which makes deployment difficult. However, Dremio is working hard on this issue.
What problems is the product solving and how is that benefiting you?
The goal with an analysis tool such as Power BI is to discover new insights and to visually present these insights so that other employees can understand them and make better decisions.
Nobody wants to deal with the acquisition or transformation of data. If you have a large amount of data, it is not practical to load this data into Power BI. Dremio enables us to perform analyses directly on the data in the Date Lake without the need for copying.
This turns a Data Lake into a database that can be queried by SQL.
Nobody wants to deal with the acquisition or transformation of data. If you have a large amount of data, it is not practical to load this data into Power BI. Dremio enables us to perform analyses directly on the data in the Date Lake without the need for copying.
This turns a Data Lake into a database that can be queried by SQL.
Recommendations to others considering the product:
Have a look at Dremio University.
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Innovative and fast growing solution
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
I'm delivering data from our data lake on top of Azure Data Lake Gen2 for both microservices through Rest API and BI tools such as Power BI. We are also using Dremio to accelerate and empower our teams to understand and take insights from data to meet business needs.
Recommendations to others considering the product:
Dremio is a very powerful and fast-growing tool and the support team is very helpful.
An effective tool, with great enterprise support.
What do you like best about the product?
* 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.
* 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.
What do you dislike about the product?
* 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.
* 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.
What problems is the product solving and how is that benefiting you?
One tool that can query and combine all our data fast despite being stored in different locations with different formats.
A new way for simplification
What do you like best about the product?
Simplification – Single point of data access.
Data Blending – Merge diverse data pools easily
Protection – Enable security and authorization.
Acceleration – Performant reporting and analysis
Data Blending – Merge diverse data pools easily
Protection – Enable security and authorization.
Acceleration – Performant reporting and analysis
What do you dislike about the product?
Different roadmap AWS and Azure and not all capabilities you have in AWS are in Azure
What problems is the product solving and how is that benefiting you?
We organized the Lake like a virtual LAB or APP. In a APP we provide for all our user the correct folder structure and all the resources they need to analyze data.
Dremio use the Data lake as a data source . From outside, Dremio looks and behaves like a relational Database
Dremio use the Data lake as a data source . From outside, Dremio looks and behaves like a relational Database
Dremio for Pon Equipment Pon Power, The Netherlands
What do you like best about the product?
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.
What do you dislike about the product?
The documentation on available functions is lacking. Dremio does not have a built-in Intellisense nor autosave.
What problems is the product solving and how is that benefiting you?
Virtual data warehousing directly on Microsoft CDM.
Data Virtualization
What do you like best about the product?
able to connect data from multiple sources via Native SQL
What do you dislike about the product?
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
- Democratizing Data
- Technical edge
Very good user experience
Following the instructions, I was able to deploy a working environment in ~10 minutes and run queries against the supplied sample S3-based dataset. The CFN-based marketplace experience is flawless. The Dremio UI is intuitive and the query performance is very impressive. If you're looking for a solution that supports running BI applications against data residing in S3 (or other data stores for cross-cloud compatibility) Dremio is definitely worth a look. I appreciate the value. I only ran into one glitch (and this may have been my error) - After you deploy the environment, make sure that you record the public or private URL that points to primary Dremio Web UI for the deployed EC2 instance in your region. Coming back to the environment a few days later following a client reboot, I couldn't figure out how to retrieve the primary endpoints for the Web UI even after exploring the marketplace and AWS console interfaces. (I suspect I missed something)
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Self service Data lake acceleration
What do you like best about the product?
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.
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 about the product?
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.
My Dremio experience as enterprise-wide data platform by big German client
What do you like best about the product?
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.
In case of any issues and challenges Dremio was very cooperative on Germany and global level to solve it.
What do you dislike about the product?
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.
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)
..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)
Efficient and User Friendly SQL layer on top of open file format
What do you like best about the product?
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.
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 about the product?
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.
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