MLOps
July 14, 2026 | 15 min read
Dask vs Spark vs Ray: Choosing the right distributed computing framework
When performance, scalability, and ML use cases drive your infrastructure decision
When performance, scalability, and ML use cases drive your infrastructure decision
The question of which framework to use, Dask, Spark, or Ray, comes down to workload type: Spark is for structured data engineering at scale, Dask is for Python-native analytics teams scaling from a single machine to a cluster, and Ray is for machine learning (ML) infrastructure that requires fine-grained graphics processing unit (GPU) scheduling and distributed training. All three are mature distributed computing frameworks, but they were built with different problems in mind, and using the wrong one for your workload creates avoidable friction. This post covers how each framework works, how they compare head-to-head across the dimensions that matter most to data science and ML teams, and how to make a defensible choice for your workload.
Large-scale ETL and SQL workloads
Spark
Structured data engineering with commercial support
Spark
Batch processing at petabyte scale
Spark
Python-native analytics at scale
Dask
Pandas or NumPy workflows on a cluster
Dask
Data preprocessing before model training
Dask
Distributed model training across GPUs
Ray
Hyperparameter search and tuning
Ray
LLM fine-tuning or inference infrastructure
Ray
Dynamic, asynchronous ML pipelines
Ray
The Apache Spark framework provides an open-source distributed computation engine developed at UC Berkeley’s AMPLab starting in 2009. Its core abstraction, the Resilient Distributed Dataset (RDD), represents an immutable, fault-tolerant collection of data partitioned across a cluster. By combining in-memory caching with lazy evaluation, Spark reduced processing latency by orders of magnitude compared to its Hadoop MapReduce predecessor.
Spark is written in Scala, with PySpark providing Python bindings. Its ecosystem includes SparkSQL for structured queries, MLlib for machine learning algorithms, GraphX for graph processing, and Spark Streaming for real-time pipelines. For data engineering and large scale data processing (particularly ETL over structured datasets), Spark remains the most mature and commercially supported option.
The Dask framework is a pure Python parallel computing library first released in 2015 by Continuum Analytics (now Anaconda). It was built around a single design principle: make distributed computation feel like native Python. Dask’s DataFrame and array interfaces mirror the Pandas and NumPy APIs, allowing data scientists to scale single-machine code to a multi-node cluster with minimal changes to their workflow.
At its core, Dask builds task graphs, directed acyclic graphs (DAG) where each node is a Python callable and each edge is a data dependency. The Dask scheduler resolves these tasks in order, parallelizing independent work across workers. Dask distributed extends this model across multiple machines, supporting DataFrames, arrays, and bags with a lightweight configuration footprint.
Ray is a distributed computing framework from UC Berkeley, designed to generalize parallel computing across any Python application. Ray is designed to be a low-level primitives layer. It provides Ray tasks (remote functions) and actors (stateful distributed processes) that can run across a ray cluster of any size and hardware composition.
The Ray framework’s strength, unlike Spark or Dask, is orchestrating complex, fine-grained, heterogeneous workloads:: ML, deep learning, and reinforcement learning. The Ray ecosystem includes Ray Tune for hyperparameter search, Ray Train for distributed model training across GPUs, Ray Serve for model inference, and RLlib for reinforcement learning at scale. Third-party libraries including Hugging Face Transformers, XGBoost, and LightGBM have added Ray integrations for GPU-accelerated, high performance parallel computing.
Ray has also become a standard infrastructure layer for large language model (LLM) workloads. LLM fine-tuning, distributed inference, retrieval-augmented generation (RAG) pipelines, and embedding generation at scale all benefit from Ray’s ability to coordinate GPU-intensive tasks across heterogeneous clusters. Teams using frameworks like vLLM for inference serving or running foundation model training with PyTorch FSDP frequently use Ray as the orchestration layer underneath.
The central question in dask vs spark is whether your team primarily uses Python-native data science or SQL-oriented data engineering.
Spark’s Catalyst Optimizer rewrites query plans before execution, and Tungsten manages low-level memory operations. For bulk operations on structured data (joins, aggregations, filtered scans), Spark consistently outperforms Dask at a very large scale. Its execution model is optimized for coarse-grained batch processing where the same transformation applies to many partitions in parallel.
Dask introduces Python serialization overhead that Spark avoids by running on the JVM. When running Python user-defined functions in PySpark, similar overhead applies, though Pandas UDFs via Apache Arrow reduce it. For NumPy-heavy or mixed-type workloads, the performance gap between the two narrows considerably.
Data preprocessing is where Dask is most competitive. Because Dask mirrors the Pandas API, teams can prototype on a local dataset and scale the same code to a multi-node cluster by swapping the scheduler. For feature engineering workflows that are primarily Pandas-based, dask distributed is the more natural and lower-friction fit.
Spark’s strength is structured, production ETL pipelines where schema enforcement, Spark SQL semantics, and fault tolerance at petabyte scale matter more than Python ecosystem compatibility.
For pure Python applications, Dask wins clearly. It has no JVM dependency, installs via pip, and runs single-machine parallel compute without any cluster configuration. Spark requires understanding the driver/executor model, the RDD paradigm, and, for Python users, the overhead of PySpark’s serialization layer. Teams with strong Python backgrounds and no existing Spark infrastructure will reach productivity faster with Dask.
When evaluating ray vs spark, the relevant dimension is not data processing scale but ML workload architecture.
Spark’s MLlib covers classical ML algorithms including regression, clustering, and tree-based methods. For deep learning and GPU-accelerated ML workloads, Spark requires the RAPIDS Accelerator and external integration work. Ray is natively suited to ML workloads spanning CPUs and GPUs. Ray Train handles distributed training, Ray Tune handles distributed hyperparameter tuning with early stopping and Bayesian optimization, and the full lifecycle runs within a single framework. Spark’s MLlib has no equivalent primitives for multi-GPU training or dynamic hyperparameter scheduling.
When evaluating dask vs ray, the comparison is between two Python-native frameworks operating at different levels of abstraction.
Dask’s task graphs represent computation as a static DAG resolved before execution, which allows the scheduler to optimize the full plan upfront. This makes Dask’s task graphs well-suited to batch data transformations with predictable structure. Ray tasks are submitted and executed dynamically at runtime, enabling reactive, event-driven workflows but making static optimization harder. Choose Dask when your computation is declarative. Choose Ray when it is dynamic.
For numerical parallel computing (large array operations, distributed matrix multiplications, Monte Carlo simulations), Dask outperforms Ray out of the box because it maps directly onto NumPy semantics. For high performance heterogeneous workloads mixing CPUs and GPUs across a cluster, Ray’s resource scheduling model is more capable. Dask on Ray, an integration that runs Dask atop a Ray cluster, gives teams Dask’s familiar API with Ray’s scheduling infrastructure, which is useful when the organization has already standardized on Ray for ML work.
Both frameworks scale across multiple machines, but Ray requires less infrastructure configuration. Ray’s resource model natively handles heterogeneous hardware including GPU nodes, while Dask requires a separate distributed scheduler setup (dask.distributed) for multi-node operation and relies on the separate dask-cuda project for GPU-aware scheduling. For teams without dedicated infrastructure engineering support, Ray typically gets to multi-node operation faster.
No single framework dominates across all use cases. The right choice depends on workload type, team expertise, and infrastructure constraints.
Choose Spark if:
Choose Dask if:
Choose Ray if:
If a dataset fits comfortably in memory on a single machine, none of these three frameworks is the right choice. The overhead of a distributed scheduler outweighs the benefit until data volume or compute cost requires a cluster.
Using all three together is common in production environments. Spark handles ingestion and ETL, Dask handles preprocessing and feature engineering, and Ray handles model training and inference orchestration. The frameworks are complementary, and Dask on Ray allows teams that have standardized on Ray's scheduling infrastructure to keep Dask's familiar DataFrame API without running a separate cluster.
The challenge is rarely selecting a single framework. Enterprise teams often need to support Spark, Dask, and Ray simultaneously, and doing that across separate cluster configurations with inconsistent environments introduces real infrastructure complexity.
Domino supports Spark, Dask, and Ray workloads within a single governed environment. No separate cluster configurations, no reprovisioning between frameworks. Teams get on-demand compute, reproducible environments, and audit trails, whether the workload is running on Spark, Dask, or Ray. For teams evaluating Spark specifically, the Apache Spark and MLOps dictionary entries cover the underlying architecture in depth.
Dask is better than Spark for Python-native analytics workloads where the team’s primary tools are Pandas and NumPy. It has no JVM dependency, installs via pip, and scales single-machine Python code to a cluster with minimal changes. Spark is better for large-scale structured data engineering: ETL pipelines, SQL workloads, and fault-tolerant batch processing at petabyte scale with commercial support requirements. Neither is universally superior: the right choice depends on whether your primary work is data engineering at very large scale (Spark) or Python data science that needs to scale out (Dask).
Apache Spark is a mature JVM-based distributed computation engine optimized for structured data processing and SQL workloads. Dask is a Python-native parallel computing library that mirrors the Pandas and NumPy APIs and scales from a single machine to a multi-node cluster with minimal configuration changes. Ray is a general-purpose distributed computing framework built for heterogeneous workloads, particularly ML, deep learning, and reinforcement learning across CPUs and GPUs. Spark handles data engineering best, Dask handles Python analytics best, and Ray handles ML infrastructure best.
Spark’s MLlib supports classical ML algorithms but lacks native primitives for distributed deep learning or GPU scheduling. Ray is built from the ground up for ML infrastructure: Ray Train handles multi-GPU model training, Ray Tune handles distributed hyperparameter search, and RLlib handles reinforcement learning at scale. Ray’s actor model also supports fine-grained, asynchronous execution that maps more naturally to the stateful, iterative workflows common in training pipelines than Spark’s synchronous partition-based model.
Both are Python-native, but they serve different purposes. Dask is better when the workload maps naturally onto NumPy or Pandas operations: data preprocessing, large-scale DataFrame analytics, or parallel scientific computing. Ray is better when the workload requires dynamic task graphs, GPU scheduling, or ML pipeline orchestration across a heterogeneous cluster. For teams that need both capabilities, running Dask on Ray is a supported integration that combines Dask’s familiar API with Ray’s scheduling infrastructure, avoiding the need to choose between them.
No. Ray and Spark address different problems. Spark is optimized for large-scale structured data processing, SQL workloads, and ETL pipelines. Ray is optimized for ML infrastructure: distributed training, hyperparameter search, and dynamic task execution across GPUs. They are increasingly used together rather than as alternatives, with Spark handling data engineering upstream and Ray handling model training and inference downstream.
Ray is the strongest choice for LLM training and inference infrastructure. Ray Train supports distributed training across multiple GPUs with native integrations for PyTorch FSDP and DeepSpeed. Ray Serve handles production inference serving, and the broader Ray ecosystem integrates with tools like vLLM, Hugging Face Transformers, and Anyscale for large-scale model workloads. Spark and Dask are not designed for distributed multi-GPU model training the way Ray is. Dask does support GPU-accelerated data processing through the dask-cuda and RAPIDS ecosystem, but that solves a different problem than training, and both frameworks are better suited to the data preprocessing stages upstream of it..
Yes. Dask on Ray is a supported integration that runs the Dask scheduler on top of a Ray cluster. This gives teams Dask’s familiar DataFrame and array API while using Ray’s resource scheduling and cluster management infrastructure. It is useful when an organization has already standardized on Ray for ML work and wants to avoid running a separate Dask cluster for preprocessing workloads.
Nikolay Manchev is a former Principal Data Scientist for EMEA at Domino Data Lab. In this role, Nikolay helped clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain-specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.