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Dremio

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Infraestructura de Datos · www.dremio.com

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Resumen

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.

El problema que resuelve Dremio

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.

Contexto para decidir

Usa estos puntos para comprobar si el producto encaja con tu operación, no solo con la lista de funciones.

  • Precio de entrada publicado: Free. Confirma límites de usuarios, uso y funciones por plan.
  • Despliegue: cloud, on_premise, hybrid. Comprueba requisitos de seguridad, residencia de datos y acceso para todos los equipos que lo utilizarán.
  • Integraciones verificadas: Amazon S3, Azure Data Lake Storage, Google Cloud Storage, AWS Glue Data Catalog, Apache Hive, Apache Iceberg REST Catalog. Valida el sentido de sincronización y los límites del plan elegido.
  • La ficha se comprobó por última vez el 31/7/2026; los precios y las funciones pueden cambiar.

Cómo evaluar Dremio

Una ficha ayuda a crear una lista corta; una prueba con el flujo real del equipo decide si la herramienta encaja. Usa esta lectura junto con los datos estructurados y confirma cualquier cambio con el proveedor.

Encaje de flujo

El registro la considera especialmente adecuada para 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. Comprueba que ese contexto coincide con el volumen, los roles y los procesos que debe soportar tu equipo.

Preguntas del piloto

  • ¿Puede Dremio completar el flujo crítico sin trabajo manual fuera de la herramienta?
  • ¿Las conexiones registradas (Amazon S3, Azure Data Lake Storage, Google Cloud Storage, AWS Glue Data Catalog) cubren el sentido de sincronización, los permisos y el volumen que necesitamos?
  • ¿Qué límites de usuarios, uso, almacenamiento, soporte o seguridad aparecen después del precio inicial?

Evidencia y vigencia

Esta ficha se comprobó el 31/7/2026. La fecha indica cuándo se revisó el registro, no una garantía de que el proveedor no haya cambiado sus condiciones después.

Ideal para

  • 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

No encaja si

  • 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

Por qué está listada

  • 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.

Precios

Community Edition

Free

Free, 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 pricing

Self-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

Funciones

AI featuresAI Semantic Layer and built-in AI Agent, plus MCP integration for Claude/ChatGPT/Gemini.
Data governance & lineageFine-grained RBAC down to rows/columns, Open Catalog governance, plus compliance certifications.
Data pipelines / ETLPositioned to reduce/eliminate traditional ETL via federated live querying rather than being a pipeline/orchestration tool.
Pre-built connectors20+ named native connectors plus ODBC/JDBC/Arrow Flight generic connectivity.
Public APIDocumented ODBC/JDBC/Arrow Flight interfaces and a Dremio CLI/MCP integration.
Role-based access controlRole-based access control with row/column-level granularity.
Scheduling & triggersNo evidence of job/workflow scheduling distinct from query execution and Reflections refresh.
Self-hosting / on-premCommunity Edition and Dremio Enterprise both support self-hosted deployment.
Workflow automationAutomation is scoped to query acceleration and AI-agent query assistance, not general workflow automation.

Integraciones

Amazon S3Azure Data Lake StorageGoogle Cloud StorageAWS Glue Data CatalogApache HiveApache Iceberg REST CatalogNessieSnowflakeDatabricks Unity CatalogMicrosoft OneLakeAmazon RedshiftGoogle BigQueryAzure Synapse AnalyticsOracle DatabasePostgreSQLMySQLMicrosoft SQL ServerMongoDBElasticsearchTableauPower BIdbtLooker

Seguridad y cumplimiento

SOC 2 Type IIISO/IEC 27001:2022HIPAAGDPRCCPA

Pros y contras

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

Contras

  • 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

Qué muestra el registro

4.6/5
69 reviews agregadasÚltima comprobación 2026-07-31
G2 ↗PeerSpot ↗

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.

Resumen y puntuación agregados de plataformas públicas de reseñas. Enlazamos a las reseñas originales en lugar de reproducirlas — lee la fuente antes de decidir.

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