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.



| Product | Market Share (%) |
|---|---|
| Dremio | 2.3% |
| Databricks | 9.6% |
| KNIME Business Hub | 8.7% |
| Other | 79.4% |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Databricks | 4.1 | 9.6% | 96% | 91 interviewsAdd to research |
| Teradata | 4.1 | N/A | 88% | 83 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 4 |
| Company Size | Count |
|---|---|
| Small Business | 196 |
| Midsize Enterprise | 86 |
| Large Enterprise | 419 |
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.
Dremio was previously known as Dremio AWS - BYOL.
UBS, TransUnion, Quantium, Daimler, OVH
| Author info | Rating | Review Summary |
|---|---|---|
| SR BI developer at BRQ Digital Solutions | 4.0 | I've used Dremio for three years to simplify data integration and BI reporting, valuing its federated queries and lineage visibility, though stability issues, upgrade bugs, and limited transformation features remain areas that need improvement. |
| Senior Software Architect at USEReady | 4.0 | I've explored Dremio primarily for proof of concept as a data virtualization application. While it's effective in connecting and visualizing data on SaaS, it lacks Salesforce connectivity and has fewer connectors compared to Starburst, which offers more robust integration options. |
| Senior Consultant - Data Analytics at a comms service provider with 201-500 employees | 4.0 | I've used Dremio primarily as a logical data warehouse to reduce storage costs and centralize data access, appreciating its interface and integration features, though I find data cataloging complex without the cloud version's Arctic support. |
| Data Analyst at a insurance company with 501-1,000 employees | 4.5 | I've used Dremio for 1.5 years to connect dashboards across multiple databases, finding its web interface fast and intuitive. It simplifies KPI tracking, though SQL syntax differences from SQL Server could be improved for new users. |
| Investment Banking Associate at Vietcap | 4.0 | We use Dremio for financial data analytics with seamless integration to various databases. It's easy to build queries, but we face performance issues, especially with MongoDB integration. Despite high licensing costs, it's cost-effective in terms of manpower. |
| Senior Software Engineer Technical Lead at Apple | 5.0 | I appreciate Dremio for visualizing BI and Tableau data simultaneously and its valuable feature of generating refresh reflections. However, improvements are needed in error handling and table performance. Despite testing alternatives, Dremio remains our preferred solution. |
| Founder/ CEO at Morphogen Data Inc. | 5.0 | I use Dremio for high-performance queries on data lakes, valuing its data lineage and provenance for compliance and machine learning. It has potential but needs improvements in its automated SQL query tool for users without SQL experience. |
| Security Data Engineer at a pharma/biotech company with 5,001-10,000 employees | 5.0 | I use Dremio for data engineering, benefiting from its capability to query files on my storage. While it functions like a data warehouse, limitations exist, notably lacking support for recursive CTEs and simplifying complex nested data, impacting my work involving hierarchical structures. |