

Snowflake and Dremio are competing in the data management and analytics sector. Snowflake holds the upper hand due to its superior scalability and flexibility, which enables efficient handling of large datasets and easy configuration adjustments.
Features: Snowflake offers superior scalability, distributed architecture for fast processing, and supports multiple file formats. Unique features like time travel and zero-copy cloning enhance data management. It operates as a managed service, providing seamless updates. Dremio excels in integrating various data sources and offers efficient data management across platforms with features such as data reflections and Apache Arrow for in-memory query operations.
Room for Improvement: Snowflake could improve its geo-spatial querying and simplify the Snowpipe auto-ingest process. More intuitive pricing tools and built-in analytics functionalities would reduce dependency on external software. Dremio needs better Delta Lake integration, faster query execution times, and more comprehensive documentation. Enhanced support for streaming data and ETL pipelines would also benefit users.
Ease of Deployment and Customer Service: Snowflake supports public, hybrid, and private cloud deployments, providing flexible hosting options. Its customer service is known for being responsive, though some users report prolonged response times and a lack of SLAs. Dremio offers strong support for hybrid and on-premises deployments, beneficial for localized data privacy needs, but users note that its response times are slower compared to more established platforms.
Pricing and ROI: Snowflake's pay-as-you-go pricing model offers flexibility but can be complex for upfront cost estimation. Despite competitive pricing, it's perceived as higher than alternatives like Redshift. Dremio is generally less costly than Snowflake, yet licensing costs for full capabilities can increase for larger deployments. While Snowflake often proves ROI with operational efficiency, Dremio's ROI is more dependent on specific infrastructure integration.
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.
Snowflake undoubtedly cost us more money and provided almost no value to us.
We experience savings of about 15%.
I would rate customer support a ten out of ten.
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.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
We sought this documentation multiple times but faced difficulty in obtaining it.
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.
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.
Dremio's scalability depends on your hardware and your deployment.
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.
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.
Snowflake is highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.
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.
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.
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.
A specific example is when they told us they fully support Iceberg, and then they have over three thousand words of bullet point text with a link to reference articles with all the limitations of their Iceberg support.
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.
I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.
I had a good experience with pricing, as the pricing seems okay to me and the client.
Dremio does have a core of the engine that is open-source, but all the important features for the enterprise version are under license.
When it comes to cloud support, the setup cost is very cheap compared to other platforms, such as Oracle or PostgreSQL, which typically require higher costs.
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.
Multiple data sources can be queried in one query, and it is not just a query engine.
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 has contributed to significant cost savings.
| Product | Mindshare (%) |
|---|---|
| Snowflake | 15.4% |
| Dremio | 4.3% |
| Other | 80.3% |


| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 6 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 20 |
| Large Enterprise | 61 |
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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