Dremio review
A flexible lakehouse for querying diverse data estates across cloud and self-hosted environments.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Dremio is an open data lakehouse platform for organizations that need governed analytics across object storage, databases, and lakehouse catalogs. Its distributed SQL engine supports federated queries across databases, warehouses, object storage, and NoSQL sources. The product also covers data engineering and AI workloads through Apache Iceberg, Delta Lake, streaming ingestion with CREATE PIPE, and integrations with engines and tools such as Apache Spark, Apache Flink, Trino, Tableau, Power BI, dbt, Looker, Excel, and Jupyter. Deployment spans managed cloud, self-managed environments, and local machines or servers.
The plan structure suits teams that want to start with a free option and expand into managed or enterprise deployment. Standard is a forever-free cloud tier with up to three projects, SQL support, and Apache Iceberg and Delta Lake support. Dremio Cloud adds a fully managed cloud lakehouse, automatic scaling and optimization, consumption-based pricing, and a 30-day trial. Dremio Enterprise is self-managed and runs on Kubernetes, on-premises, or in the cloud, adding advanced security and governance plus enterprise support. The free tier can still involve customer cloud infrastructure costs, while enterprise pricing is contact-sales.
Dremio’s strongest fit is a data estate that spans multiple systems and needs one SQL layer with governance and catalog flexibility. Role-based access control, row filters, column masking, Reflections, Iceberg table clustering, and automatic maintenance support both access control and query performance. Its Open Catalog is powered by Apache Polaris, and the platform connects with AWS Glue Data Catalog, Snowflake Open Catalog, and Unity Catalog. Choose Dremio for broad federation and open table-format support; teams seeking only a narrow single-source analytics tool may find this scope unnecessary.
Dremio pros and cons
- Where it wins
- Federated queries across databases, warehouses, object storage, and NoSQL
- Native Apache Iceberg support with Polaris-powered Open Catalog
- Cloud, self-managed, Kubernetes, on-premises, and API deployment options
- Where it doesn't
- Enterprise pricing requires contacting sales
- Cloud usage is consumption-based and measured in DCUs
- Customer cloud infrastructure costs may still apply on the free tier
Dremio fact sheet, pricing and score →
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