

Snowflake and Dremio compete in the data management tools category. Snowflake currently holds the upper hand with its extensive features, scalability, and flexibility.
Features: Snowflake offers scalability, dynamic partitioning, and support for multiple file formats, enhancing its flexibility and performance. Dremio excels at integrating with various databases and provides a single system for data cataloging and visualization.
Room for Improvement: Snowflake could improve on geospatial queries, UI enhancements, and cost estimation. Dremio needs to enhance SQL support, connector capabilities, and execution speed for complex queries.
Ease of Deployment and Customer Service: Snowflake is widely deployed across cloud environments and is praised for its responsive customer support, though it may lack SLAs. Dremio typically operates in hybrid and on-premises setups, and while its support is solid, delays may occur due to a smaller team.
Pricing and ROI: Snowflake's pay-per-use model can be cost-effective in small deployments but may become expensive if not managed. Dremio's pricing is competitive, though it can also become costly if improperly aligned with use scale.
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.
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.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
I rate Dremio a nine in terms of stability.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake is very stable, especially when used with AWS.
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.
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.
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.
Cost reduction is one area I would like Snowflake to improve.
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.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift.
One key feature is the separation of compute and storage, which eliminates storage limitations.
Being able to perform AI and Machine Learning in the same location as the data is quite advantageous.
| Product | Market Share (%) |
|---|---|
| Snowflake | 17.0% |
| Dremio | 7.9% |
| Other | 75.1% |


| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 20 |
| Large Enterprise | 57 |
Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.
At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.
What Are Dremio's Key Features?
What Benefits Should Users Expect?
When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.
How is Dremio Implemented Across Different Industries?
Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.
What About Dremio’s Pricing, Licensing, and Support?
Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.
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?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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