Apache DataFusion alternatives

3 tools to consider instead of Apache DataFusion, shown against it.

Apache DataFusion DuckDB Polars clickhouse-local
Vendor Apache Software Foundation DuckDB Foundation Polars (open-source project, commercial backing from Polars Inc.) ClickHouse, Inc. (open-source project)
Pricing model Open source + paid options Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes Yes
Deployment Self-hosted Self-hosted Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (MIT) Yes (MIT) Yes (Apache-2.0)
Best for Engineers building a custom database, DataFrame library, or streaming engine who don't want to write a query executor from scratch. Analysts and engineers running fast local SQL analytics on files without standing up a database server. Python and Rust developers wanting faster, memory-efficient local DataFrame processing than pandas. Engineers wanting fast, one-off SQL analytics or format conversion over files without running a database server.
Pricing

Free and open source under the Apache 2.0 license; no vendor pricing, as it is a library rather than a hosted product.

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

Free and open source under the MIT license; a hosted/hybrid cloud version is sold separately by MotherDuck.

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

The core Polars library is free and open source under the MIT license; Polars Cloud is a separate commercial offering for distributed scale-out.

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

Free and open source as part of the ClickHouse distribution; no separate pricing or license.

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

Features
  • Embeddable, extensible query engine written in Rust
  • Vectorized, multi-threaded execution on Apache Arrow
  • SQL and DataFrame APIs for Rust and Python
  • Native Parquet, CSV, JSON, and Avro readers
  • Pluggable table providers for custom data sources
  • Cost-based query optimizer and physical planner
  • Used as the execution core inside other databases and tools
  • In-process embedded SQL engine, no server required
  • Vectorized columnar execution
  • Direct querying of CSV, Parquet, JSON and Iceberg/Delta files
  • Bindings for Python, R, Java, Node.js and more
  • ACID transactions on local databases
  • Extensions for HTTP(S)/S3 access and spatial data
  • In-process, multi-threaded DataFrame engine written in Rust
  • Lazy query API with query-plan optimization
  • Apache Arrow-based columnar memory format
  • Streaming execution for datasets larger than memory
  • Native Parquet, CSV, JSON, and Arrow I/O
  • Built-in SQL interface alongside the expression API
  • Python, Rust, and Node.js bindings
  • Single-binary CLI, no server or cluster to install
  • Full ClickHouse SQL dialect and function library
  • Direct querying of CSV, Parquet, JSON, ORC, and Arrow files
  • Reads from local disk, HTTP, and S3-compatible object storage
  • Vectorized, multi-threaded columnar execution
  • Format conversion between supported file types
  • Scriptable for shell pipelines and ad hoc log analysis

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