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.
Product | Market Share (%) |
---|---|
Dremio | 3.3% |
Databricks | 14.5% |
KNIME Business Hub | 12.3% |
Other | 69.9% |
Title | Rating | Mindshare | Recommending | |
---|---|---|---|---|
Databricks | 4.1 | 14.5% | 96% | 91 interviewsAdd to research |
Teradata | 4.1 | N/A | 87% | 76 interviewsAdd to research |
Dremio has significantly enhanced the speed and efficiency of data processing, leading to quicker data access and analysis. Many users highlighted the cost savings associated with reduced infrastructure needs due to Dremio's ability to query data directly from data lakes. The platform's ability to integrate with existing systems without extensive restructuring or additional storage costs was frequently appreciated.
Company Size | Count |
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Small Business | 1 |
Midsize Enterprise | 3 |
Large Enterprise | 3 |
Company Size | Count |
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Small Business | 194 |
Midsize Enterprise | 92 |
Large Enterprise | 527 |
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.
UBS, TransUnion, Quantium, Daimler, OVH
Author info | Rating | Review Summary |
---|---|---|
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. |
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. |
Sr Manager at a transportation company with 10,001+ employees | 3.5 | We are using Dremio to build a data framework and data queue alongside DBT and Databricks, but integrating it with Databricks poses authentication challenges. We are still exploring its features and deploying it across various departments. |
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. |
Senior Data Engineer at AppsFlyer | 3.5 | No summary available |
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. |
Database Engineer at a tech services company with 201-500 employees | 4.0 | No summary available |