Audyense
Dremio
VerifiedData Infrastructure · www.dremio.com
Overview
Dremio is a data lakehouse platform built around Apache Iceberg and Apache Arrow that lets analysts and engineers query data in place across cloud storage, data warehouses, and relational databases via a single SQL interface. It offers a semantic layer and an acceleration/caching feature called 'Reflections' for sub-second BI query performance, plus AI Semantic Layer and AI agent/MCP integration. It ships as a fully managed cloud service, a self-managed enterprise product, and a free open-source Community Edition.
The problem Dremio solves
Data teams that need to analyze information scattered across cloud object storage, warehouses like Snowflake or Redshift, and operational databases usually end up building and maintaining brittle ETL pipelines just to get everything into one place before they can query it. Dremio solves this by letting them run federated SQL directly across those sources in place, using an acceleration layer to keep BI-tool queries fast, so analytics teams stop waiting on data engineering to move data before they can answer a question.
Decision context
Use these points to test whether the product fits your operation, not just whether it has a long feature list.
- Published starting price: Free. Confirm user, usage, and feature limits for the plan you would actually buy.
- Deployment: cloud, on_premise, hybrid. Check security, data-residency, and access requirements for every team that will use it.
- Verified integrations include Amazon S3, Azure Data Lake Storage, Google Cloud Storage, AWS Glue Data Catalog, Apache Hive, Apache Iceberg REST Catalog. Validate sync direction and plan limits for the connections that matter.
- This record was last checked on 7/31/2026; pricing and features can change.
How to evaluate Dremio
A listing helps create a shortlist; a trial with the team’s real workflow decides whether the tool fits. Use this reading with the structured facts and confirm changes with the vendor.
Workflow fit
The record describes it as a fit for Data engineering / analytics teams that need to query data across multiple disparate sources without building and maintaining ETL pipelines, Organizations standardizing on Apache Iceberg and wanting an open lakehouse architecture instead of vendor lock-in, Mid-market to enterprise teams that need self-hosted/on-prem or hybrid deployment for compliance or infrastructure-control reasons. Check that this context matches the volume, roles, and processes your team needs it to support.
Pilot questions
- Can Dremio complete the critical workflow without manual work outside the product?
- Do the recorded connections (Amazon S3, Azure Data Lake Storage, Google Cloud Storage, AWS Glue Data Catalog) support the sync direction, permissions, and volume we need?
- What user, usage, storage, support, or security limits appear after the headline starting price?
Evidence and freshness
This record was checked on 7/31/2026. That date tells you when the record was reviewed, not that the vendor has left its terms unchanged since then.
Best for
- Data engineering / analytics teams that need to query data across multiple disparate sources without building and maintaining ETL pipelines
- Organizations standardizing on Apache Iceberg and wanting an open lakehouse architecture instead of vendor lock-in
- Mid-market to enterprise teams that need self-hosted/on-prem or hybrid deployment for compliance or infrastructure-control reasons
Not a fit if
- Small teams or startups without dedicated data engineering resources, given the reported operational complexity
- Teams wanting a simple, fully turnkey BI tool rather than a lakehouse query/virtualization platform
Why it’s listed
- Federated SQL queries across disparate data sources without moving/duplicating data, reducing reliance on traditional ETL pipelines.
- Genuine free, unlimited Community Edition alongside fully managed cloud and self-managed enterprise options.
Pricing
Community Edition
FreeFree, open-source Dremio query engine for local machines or self-managed servers.
- Apache Iceberg lakehouse query engine
- Federated SQL queries across data sources
- Self-hosted / local deployment
Dremio Cloud
$0 per DCU (consumption-based)Fully managed lakehouse platform, billed by compute consumption.
- Fully managed infrastructure with automatic scaling
- AI Semantic Layer and AI Agent capabilities
- Intelligent Query Engine + Reflections acceleration
- Open Catalog (Apache Polaris) support
Dremio Enterprise
Custom pricingSelf-managed lakehouse platform deployable on Kubernetes, on-premises, or across any cloud.
- Self-managed security and access controls
- Flexible deployment: Kubernetes, on-prem, or any major cloud
- Same AI feature set as Cloud
Features
Integrations
Security & compliance
Pros & cons
Pros
- Federated SQL queries across many disparate data sources without moving or duplicating data
- Reflections acceleration feature delivers sub-second BI query response times
- Rated highly for ease of use and fast, direct data exploration by both technical and non-technical users
- Strong data lake / lakehouse integration and cloud processing scores from reviewers
Cons
- Some users report occasional out-of-memory (OOM) errors without clear diagnostic explanations
- High resource demands and difficulty maintaining the environment at scale
- Steep learning curve for advanced features despite a low barrier for basic querying
- Review volume on major platforms is relatively thin for a company of Dremio's market position
What we found
On G2, Dremio holds a 4.6-out-of-5 rating with especially strong marks for ease of use, data querying, and cloud processing, though the sample size is modest for a platform this broadly deployed.
Ratings and review counts come from public review platforms. We link to the original source and keep the underlying review text out of this profile.
User reviews
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