Dremio
Enables organizations to query data lakes directly with high performance SQL without moving or copying data, reducing costs and complexity while accelerating analytics workloads.
Last updated Feb 8, 2026
- Business Model
- SaaS
- Target Market
- Enterprise
- Employee Count
- 201-500
- Funding
- $414M
- API Available
- Yes
Market Position
Leading data lakehouse platform provider competing in the modern data stack and cloud data platform market
Overview
Dremio is not an AdTech company. It is a data lakehouse platform company that provides a high-performance SQL query engine for data lakes and lakehouse architectures. Founded in 2015, Dremio enables organizations to query data directly in open formats like Apache Iceberg, Delta Lake, and Apache Parquet without moving data into traditional data warehouses. The platform combines the flexibility and cost-effectiveness of data lakes with the performance and functionality of data warehouses. Dremio's technology is used across various industries including financial services, healthcare, retail, and technology companies for analytics, business intelligence, and data science workloads. While Dremio is not part of the AdTech ecosystem, its data platform could potentially be used by AdTech companies for managing and analyzing advertising data, campaign performance metrics, and customer data. The company competes in the data lakehouse and data platform market rather than the advertising technology space.
Products & Features
Dremio Cloud
Fully managed cloud service for data lakehouse analytics
Dremio Software
Self-managed deployment option for on-premise or cloud environments
Apache Arrow-based Query Engine
High-performance SQL query engine built on Apache Arrow
Semantic Layer
Business-friendly data modeling and curation layer
Data Reflections
Automatic query acceleration through intelligent data optimization
Key Features
Direct query on data lake storage (S3, ADLS, HDFS)Apache Iceberg support and catalogSQL query engine with sub-second performanceSelf-service data access for analystsAutomatic query accelerationData virtualization and federationGit-like data versioningBI tool integration
Use Cases
Interactive analytics on data lakesBusiness intelligence and reportingData science and machine learningSelf-service analyticsData warehouse modernizationMulti-cloud data access
Customer Segments
Enterprise data teamsData engineersData analystsBusiness intelligence teamsData scientists