

Darwin and Dremio compete in the data analytics and management tools category. Darwin appears to have an advantage in model quality and rapid iteration, while Dremio's strengths lie in data governance and compliance.
Features: Darwin is notable for its quick data modeling, extensive data cleaning capabilities, and efficient model iteration. Meanwhile, Dremio excels in managing data across varied storage systems, using memory efficiently for operations, and providing robust data lineage and governance features.
Room for Improvement: Darwin could improve its automatic model deployment and integration with data repositories like AWS. Enhancements in user-friendly dashboards and the ability to offer more user-driven suggestions rather than automatic ones are needed. Dremio needs to refine its execution times for large queries, improve Delta I/O connector support, and enhance ease of use for data cataloging in community versions.
Ease of Deployment and Customer Service: Darwin supports public, private, and on-premises cloud deployments with responsive customer service renowned for quick resolutions. Dremio offers strong hybrid and public cloud deployment options but can perform poorly on single nodes. Both companies are praised for helpful support, though Darwin's technical assistance receives particular commendation.
Pricing and ROI: Darwin provides significant value compared to hiring a full team of data scientists with reported improvements in efficiency and productivity, albeit with potentially high licensing costs. Dremio is more affordable than some competitors like Snowflake but is still considered expensive by certain users, lacking pricing transparency which complicates ROI assessments.
Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.
We contact the technical support at least weekly; currently, it's more or less daily because we face performance issues, crashes of queries sometimes, and we also need support for configuration of the query engines and so on.
I would rate customer support a ten out of ten.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
Dremio's scalability can handle growing data and user demands easily.
Dremio's scalability depends on your hardware and your deployment.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
We refactored the query, and that worked, but it still doesn't make you confident to go to production with a product that might crash depending on the query you send it.
I rate Dremio a nine in terms of stability.
Before we wrap up, I want to add that Dremio is lacking a semantic layer in a manner that the user can just choose dimensions, metrics, and filters to query the data.
Starburst comes with around 50 connectors now.
I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.
I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.
Dremio does have a core of the engine that is open-source, but all the important features for the enterprise version are under license.
I had a good experience with pricing, as the pricing seems okay to me and the client.
Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.
Reflections and automatic reflections speed up queries automatically based on usage, which means it detects what dimensions and measures are used the most and then materializes the answers upfront, speeding up the whole BI and analytics use case.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
| Product | Mindshare (%) |
|---|---|
| Dremio | 2.0% |
| Darwin | 1.5% |
| Other | 96.5% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 6 |
| Large Enterprise | 5 |
Darwin offers advanced features like automated model-building, data cleaning, and rapid iteration, designed for efficient and intuitive use, enhancing productivity through easy system integration and model optimization.
Darwin caters to enterprises needing robust data management and streamlined model development. It provides tools for evaluating dataset quality and resolving data issues such as missing entries or incorrect types. With its REST API, it integrates seamlessly into existing systems, empowering rapid model optimization. While users find its interface intuitive, there is a demand for more advanced functionalities such as direct data access through APIs and enhancements in non-supervised models. The platform's educational resources and transparency in operations are areas identified for further improvement, along with user-friendly enhancements to dashboards.
What are Darwin's Most Important Features?Darwin is instrumental in industries like lending, where it's used for predicting credit defaults and managing risk portfolios. It supports client segmentation and delinquency assessment, allowing firms to analyze data for preemptive actions. Additionally, it's effective in sectors such as oil, gas, and aerospace for data analysis, supply chain optimization, and model creation, promoting efficient processes and reducing dependence on specialist skills.
Dremio offers a unified data platform simplifying data management with features like data virtualization, automatic query acceleration, and role-based access management, catering to lakehouse data platforms and integrating multiple sources efficiently.
Dremio functions as an advanced analytics platform, seamlessly integrating multiple data sources for building lakehouse architectures and enhancing business intelligence analytics. Leveraging comprehensive query performance and data federation capabilities, it serves as a central hub for managing data marts and high-performance querying from data lakes. The platform supports BI tools such as Tableau and Power BI, providing a cohesive environment for data analysis. While supporting compliance through data lineage and providence, Dremio enhances user experiences with its intuitive interface and robust analytics functionality. However, it needs improvements in ETL pipelines, semantic layer flexibility, performance optimization, especially with nested queries, and easier handling of complex SQL queries.
What are Dremio's key features?Industries leverage Dremio for advanced data solutions, particularly in financial analytics where managing data zones, maintaining Iceberg tables, and performing complex analyses are crucial. By integrating seamlessly with data lakes and BI tools, organizations can streamline their analytics workflows to derive actionable insights efficiently.
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