Dremio alternatives

3 tools to consider instead of Dremio, shown against it.

Dremio Apache Iceberg Databricks Cloudera Data Platform
Vendor Dremio Corporation Apache Software Foundation Databricks, Inc. Cloudera, Inc.
Pricing model Usage-based Open source + paid options Usage-based Quote only
Free tier Yes Yes Yes No
Deployment Cloud, Self-hosted Self-hosted Cloud Cloud, Self-hosted
Open source No Yes (Apache-2.0) No No
Best for Teams wanting to run governed SQL analytics directly on lake data without a separate warehouse copy. Teams building a multi-engine lakehouse who need one open table format multiple compute engines can share. Organizations wanting a single managed platform spanning data engineering, SQL analytics and machine learning on Spark. Large regulated enterprises with existing Hadoop-ecosystem investments needing hybrid or on-premises lakehouse deployment.
Pricing

Dremio Cloud is billed per Dremio Compute Unit (DCU) with a 30-day free-credit trial; Dremio Enterprise/Software pricing requires contacting sales.

Dremio Cloud $0.20 per DCU
Dremio Enterprise Contact sales

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Free, open-source specification and libraries; no vendor pricing, though managed catalog and compute services built on Iceberg are sold separately by cloud vendors.

Pricing has not been verified yet — see the vendor's site.

Pay-as-you-go pricing metered in Databricks Units (DBUs) per second, varying by workload type and tier, plus separate underlying cloud infrastructure costs; committed-use contracts offer discounts. A limited free Community Edition exists.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Consumption-based licensing (Cloudera Consumption Units) across compute and services; public pricing figures are not published and require contacting Cloudera sales.

Pricing has not been verified yet — see the vendor's site.

Features
  • SQL query engine directly over data-lake and database sources
  • Native Apache Iceberg table format support
  • Apache Arrow-based in-memory columnar processing
  • Reflections: auto-maintained materializations for query acceleration
  • Semantic layer with virtual datasets and governed spaces
  • Data-as-code versioning via Nessie catalog integration
  • Cloud (managed) and self-hosted software deployment options
  • ACID transactions with snapshot isolation on data-lake files
  • Time travel and rollback to previous table snapshots
  • In-place schema evolution (add, drop, rename, reorder columns)
  • Hidden partitioning with partition evolution without rewriting data
  • Engine-agnostic: readable/writable by Spark, Trino, Flink, and more
  • Manifest-based metadata avoiding costly file-listing operations
  • Support for Parquet, ORC, and Avro file formats
  • Managed Apache Spark clusters with the Photon execution engine
  • Unity Catalog for governance and lineage
  • Delta Lake open table format
  • Notebook-based collaborative workspace
  • MLflow for experiment tracking and model deployment
  • Databricks SQL for warehouse-style BI workloads
  • Unified data warehouse, data engineering, ML, and streaming services
  • Shared governance and security via SDX (Shared Data Experience)
  • Hybrid and multi-cloud deployment, including on-premises
  • Built on open-source Hadoop-ecosystem components (Spark, Hive, Kafka, NiFi)
  • Cloudera Machine Learning workbenches for data science teams
  • Fine-grained data lineage and cataloging across workloads
  • Support for open table formats including Apache Iceberg

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