

Snowflake and Dremio are prominent players in the data management category, each with a distinct edge. Snowflake appears to offer superior scalability and flexibility, critical for handling extensive datasets, while Dremio provides a unique advantage in data cataloging and governance capabilities.
Features: Snowflake is known for its exceptional flexibility, allowing easy changes to node sizes and multi-cluster environments that enhance system performance. Its scalability, dealing effectively with large datasets, complements its distributed architecture for high-speed data processing. Dremio excels in managing data cataloging and visualization within a single system, offering federated query capabilities that enhance data access efficiency. Its robust data lineage features support strong compliance and governance, which are crucial for certain industries.
Room for Improvement: Snowflake might benefit from clearer pricing strategies, improved ETL capabilities, and better built-in analytical functions. Enhancements in user-side implementation and smoother integration with other data systems also present important improvement areas. Dremio could improve its scalability, support for Delta connectors, and large query execution efficiency. Stronger documentation and expanded connector support would enhance user experience and integration capabilities.
Ease of Deployment and Customer Service: Snowflake typically operates on public and hybrid clouds, with generally stable and responsive support, though some customers perceive a lack of SLAs. Dremio offers deployment across public, hybrid, and on-premises environments, providing solid customer support, though its response times are sometimes slower than desirable for enterprise clients.
Pricing and ROI: Snowflake employs a dynamic pricing model based on use and storage, perceived as reasonable but sometimes complex. Users often cite significant ROI in operational efficiencies and cost savings. Dremio is noted for its cost-effectiveness versus competitors, although concerns exist regarding scaling costs from its community to licensed versions. Calculating ROI is challenging for both due to the multiplicity of costs and dependencies on supplementary products.
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.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.
The technical support from Snowflake is very good, nice, and efficient.
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.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.
I rate Dremio a nine in terms of stability.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
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.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
What things you are going with to ask the support and how we manage the relationship matters a lot.
If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
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.
We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
| Product | Market Share (%) |
|---|---|
| Snowflake | 16.1% |
| Dremio | 7.1% |
| Other | 76.8% |


| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 20 |
| Large Enterprise | 57 |
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.
Snowflake provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?
What Benefits Should You Look for?
In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
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