Dremio

Dremio offers an open data lakehouse for data management and catalog services based on Apache Iceberg, Arrow, and Polaris. Founded in 2015 and headquartered in Santa Clara, CA, Dremio has built a strong business helping companies manage and consume distributed data for analytics without the need for costly migrations. Dremio’s platform queries data across hybrid or multi-cloud environments, with deployment options both on premises and in the cloud. This includes Dremio Cloud SaaS for AWS and Microsoft Azure.

Key capabilities include the following.

• Governed semantic layer: Applies a SQL engine, catalog, and agentic tools to Apache Iceberg tables so organizations can organize and query data in cloud object storage or other lakehouse environments.
• Warehouse cost reduction and modernization: Enables teams to run BI and analytics workloads directly on open table formats in data lakes, which can reduce reliance on proprietary data warehouses or legacy Hadoop-based systems.
• Hybrid and multi-cloud data access: Allows analysts, applications, and agents to query data across on-premises and cloud environments without centralizing all data in a single platform.
• Real-time data access for AI and analytics: Transforms datasets, creates data products, and materializes views in memory for fast, efficient consumption.
• Performance optimization: Analyzes workloads, pre-computes query results, and automatically tunes the lakehouse environment with minimal human effort.

Dremio’s SQL query engine integrates natively with Apache Iceberg tables and leverages Apache Arrow columnar processing to support high-performance analytics at scale while controlling infrastructure costs. The platform also supports AI and machine-learning workflows through integrations with analytics and MLOps tools. Dremio serves hundreds of global enterprises and employs about 350 people worldwide. The company maintains a broad ecosystem of cloud, technology, and consulting partners. It co-founded or contributed to all three open-source projects on which its services run.

Dremio made significant progress with customers over the last year: 35% said they had no significant problems, up from 14% in 2025 and beating the peer average of 26%. Concerns about stability also eased by a wide margin. About a third (35%) of users did cite problems with complexity, more than twice the survey average and nearly twice the percentage of last year. Dremio scores improved in several areas vs. 2025. It rose from average to the #1 position in the categories of Customer Satisfaction and User Experience in the Data Platforms peer group. And its Technical Foundation score rose from below average to the top rank in the same peer group.

Dremio is best suited for enterprises that want an open lakehouse strategy based on Apache Iceberg and are seeking to reduce dependence on proprietary cloud data warehouses. Its primary buyers are heads of data engineering, analytics platform owners, and enterprise architects who want high-performance SQL analytics across hybrid and multi-cloud object storage without centralizing all data. Typical use cases include modernizing legacy Hadoop or warehouse environments, building governed semantic layers for BI and AI, enabling cross-cloud analytics, and supporting real-time data products for machine learning and agentic applications. Organizations that prefer fully managed, tightly bundled cloud data warehouse services might find Dremio less well suited to their needs.