

Darwin and Dremio are prominent competitors in data management and analysis technology. Darwin has the upper hand in automated model-building and quickly iterating models, while Dremio excels in data integration and federated querying capabilities.
Features: Darwin specializes in automated model-building, saving time and increasing productivity. It also offers first impressions of datasets and suggests data improvements. Dremio manages data integration from multiple storage systems and enhances data access with tools like Nessie for data lineage and Arrow for in-memory operations, and offers federated queries.
Room for Improvement: Darwin faces challenges with data integration and model operationalization, needing improvements in unsupervised model support and dashboards. Automatic dataset assessments require refinement. Dremio needs to improve complex query execution, connector support like Delta, and SQL generation. Its documentation and support for advanced SQL need enhancement.
Ease of Deployment and Customer Service: Darwin is primarily used on public cloud platforms, with strong technical support and responsive customer service from SparkCognition. Dremio supports varied deployment options, facing challenges with support documentation and Databricks integration. Both provide responsive support, with Darwin having a slight edge in user satisfaction.
Pricing and ROI: Darwin's pricing aligns with its productivity gains and cost-effectiveness, noted for reducing the need for extensive data science teams. Dremio's cost is competitive, offering value in handling large data efficiently, though its licensing can be pricey. Both demonstrate a positive ROI, driven by operational efficiencies and enhanced decision-making capabilities.
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 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.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
I rate Dremio a nine in terms of stability.
Starburst comes with around 50 connectors now.
It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
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.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
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.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
| Product | Market Share (%) |
|---|---|
| Dremio | 2.3% |
| Darwin | 1.0% |
| Other | 96.7% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.
Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.
Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.
What are Dremio's most valuable features?Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.