Apache Spark alternatives

4 tools to consider instead of Apache Spark, shown against it.

Apache Spark Apache Flink Trino Ray Dask
Vendor Apache Software Foundation Apache Software Foundation Trino Software Foundation Anyscale (Ray open-source project) Dask (NumFOCUS-sponsored open-source project)
Pricing model Open source + paid options Open source + paid options Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes Yes Yes
Deployment Self-hosted Self-hosted Self-hosted Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0) Yes (BSD-3-Clause)
Best for Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines. Teams building real-time, low-latency streaming pipelines that feed operational or analytical stores. Teams needing a single SQL query layer across data already spread across multiple systems. ML engineering teams scaling model training, tuning or inference beyond a single machine. Python/pandas teams scaling existing analysis or ML code beyond a single machine without switching ecosystems.
Pricing

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

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

Free and open source under the Apache Software Foundation; managed hosting is available through third-party cloud platforms.

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

Free and open source; commercial managed and enterprise-supported distributions are sold separately by Starburst.

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

Free and open source; a managed platform for running Ray at scale is sold separately by Anyscale on a usage-based basis.

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

Free and open source; managed cluster hosting for Dask is sold separately by Coiled on a usage-based basis.

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

Features
  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats
  • True event-at-a-time stream processing with exactly-once guarantees
  • Event-time processing and watermarking
  • Stateful processing with built-in checkpointing
  • Flink SQL for declarative pipeline authoring
  • Unified batch and streaming model
  • High-throughput, low-latency execution
  • Federated SQL queries across heterogeneous data sources
  • Pluggable connector architecture (Iceberg, Hive, Kafka, JDBC sources)
  • Massively parallel, in-memory distributed execution
  • ANSI SQL compatibility
  • Cost-based query optimizer
  • Fine-grained access control via connectors
  • Distributed task and actor execution for Python
  • Ray Train for distributed model training
  • Ray Tune for distributed hyperparameter search
  • Ray Serve for model serving
  • Ray Data for distributed data loading/preprocessing
  • Dynamic autoscaling across CPU/GPU clusters
  • Parallelizes pandas, NumPy and scikit-learn code with minimal rewrites
  • Dynamic task-graph scheduling
  • Scales from a laptop to a distributed cluster
  • Dask-ML for distributed model training
  • Integrates with Kubernetes, YARN and HPC job schedulers
  • Lazy evaluation for large-than-memory datasets

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