---
type: concept
x-llmwiki:
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  contentHash: b2fa0a10f31546b6c02c1e63f5513454e7f029b8afcee4b32d45c767e05ee12b
  pageDirectory: concepts
  sources:
    - when-to-use-spark-vs-ray-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - data-parallelism-spark
    - DP(
    - Data Parallelism
    - Data parallelism
    - data parallelism
    - DataParallel
    - data parallel
  citations:
    - file: when-to-use-spark-vs-ray-databricks-on-aws.md
title: Data Parallelism (Spark)
description: Spark excels at data parallelism — applying the same operation to each element of a large dataset — making it ideal for ETL, analytics, feature engineering, and large-scale data processing.
tags:
  - spark
  - parallelism
  - distributed-computing
timestamp: "2026-06-19T23:25:48.438Z"
---

# Data Parallelism (Spark)

**Data Parallelism** is a distributed computing paradigm in which the same operation is applied to every element of a large dataset in parallel. In Apache Spark, data parallelism is the core execution model: a dataset is partitioned across multiple nodes, and each partition is processed independently by the same function or transformation. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]

## Overview

Spark is optimized for data parallelism, making it the recommended choice for most large-scale data processing workloads. Unlike [Task Parallelism (Ray)](/concepts/task-parallelism-ray.md), where independent tasks run concurrently, data parallelism focuses on applying a uniform operation across all elements of a dataset. This model is particularly efficient for operations such as table joins, filtering, and aggregation. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]

## When to Use Spark for Data Parallelism

Spark's data parallelism is well-suited for the following use cases:

- **Large-scale data processing**: For extensive data processing tasks, Spark is highly recommended due to its optimization for operations like joins, filtering, and aggregation. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]
- **ETL and analytics**: Extract-transform-load (ETL) pipelines, analytics reporting, feature engineering, and data preprocessing all benefit from Spark's data-parallel execution model. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]
- **Machine learning at scale**: Spark's MLlib and SparkML libraries are optimized for large-scale machine learning algorithms and statistical modeling, leveraging data parallelism for training and inference. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]

## Comparison with Ray

While both Spark and Ray can run in the same Databricks execution environment, they excel at different parallelism patterns:

| Aspect | Spark | Ray |
|--------|-------|-----|
| **Parallelism model** | Data parallelism | Task parallelism |
| **Best for** | Applying the same operation to each element of a large dataset | Running independent tasks concurrently |
| **Typical workloads** | ETL, analytics, feature engineering, data preprocessing | Reinforcement learning, hyperparameter search, deep learning training, simulation modeling |

^[when-to-use-spark-vs-ray-databricks-on-aws.md]

## Combining Spark and Ray

In Databricks, you can run Spark and Ray in the same environment to leverage both frameworks within a single application. Common patterns include:

- **Spark for data handling, Ray for computation**: Use Spark to manage input and output data operations, then pass data to Ray for computationally intensive tasks. For example, use `databricks.ray.data.from_spark` to transfer training data from Spark to Ray Data. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]
- **Ray inside Spark functions**: Run Ray within Spark UDFs or Structured Streaming `foreachBatch` operations for advanced workflows. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]
- **Concurrent operations**: Run Spark operations alongside Ray functions, such as querying data with Spark while Ray is still processing. ^[when-to-use-spark-vs-ray-databricks-on-aws.md]

### Resource Management

When combining Spark and Ray, resource conflicts are rare due to task scheduling but can be managed by configuring resource allocation. The following example shows how to split resources between Spark and Ray when starting a Ray cluster: ^[when-to-use-spark-vs-ray-databricks-on-aws.md]

```python
from ray.util.spark import setup_ray_cluster, shutdown_ray_cluster

# For a Databricks cluster configured with autoscaling enabled,
# the minimum worker nodes of 4 and maximum of 6 nodes.
# 2 Spark-only nodes will launch when needed.
# The Ray cluster will have 4 nodes allocated for its use.
setup_ray_cluster(
  min_worker_nodes=4,
  max_worker_nodes=4,
)

# Pass any custom Ray configuration with ray.init
ray.init()
```

## Related Concepts

- [Task Parallelism (Ray)](/concepts/task-parallelism-ray.md) — The complementary parallelism model for independent concurrent tasks.
- Apache Spark — The distributed computing framework that implements data parallelism.
- [Distributed Data Processing](/concepts/distributed-data-parallelism-training.md) — The broader category of parallel computation across multiple nodes.
- ETL Pipelines — A common use case for Spark's data parallelism.
- MLlib — Spark's machine learning library optimized for data-parallel execution.
- [Ray on Databricks](/concepts/ray-on-databricks.md) — Running Ray alongside Spark for mixed workloads.

## Sources

- when-to-use-spark-vs-ray-databricks-on-aws.md

# Citations

1. [when-to-use-spark-vs-ray-databricks-on-aws.md](/references/when-to-use-spark-vs-ray-databricks-on-aws-bddbc4fb.md)
