dagster-spark
Package for Spark Dagster framework components.
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster and need to integrate Spark workloads. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is permissively licensed. It is worth installing as a bridge between Spark and Dagster's orchestration model, though you should verify that your target Spark version is supported.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14); Spark runtime must be available in your environment.
- Low friction installation as a pure Python wheel with a single runtime dependency on dagster.
- Actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 licensed under a permissive model, allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 563,554 downloads/mo, #5,979 on PyPI
Alternatives
Verify before relying
pip install dagster-spark
import dagster as dg
from dagster_spark import spark_resource
@dg.asset
def my_spark_asset(context) -> None:
spark = context.resources.spark
# Use spark session for Spark operations- Specific Spark versions supported or tested by this integration
- Whether the package handles Spark cluster deployment or only local/existing clusters
- Performance characteristics or scaling limits for large Spark workloads
What it is and what it does
Dagster-spark is an integration library that brings Apache Spark into Dagster's data orchestration ecosystem. It allows you to define Spark-based computations as Dagster assets—Python functions that produce data assets—and orchestrate them alongside other data operations within Dagster's declarative programming model. The package bridges Spark's distributed compute engine with Dagster's asset tracking, lineage, and observability features.
You use it to build data pipelines where some assets are computed via Spark jobs, while maintaining unified visibility and control through Dagster's web UI and orchestration engine. It integrates with Dagster's testing framework and supports the full development lifecycle from local testing to production deployment, letting you treat Spark workloads as first-class citizens in a broader data asset graph.
Use it for
- Build and orchestrate large-scale ETL pipelines using Spark within Dagster's asset framework
- Define Spark transformations as reusable data assets with automatic lineage tracking
- Test Spark-based computations locally during development before deploying to production
- Monitor and observe Spark job execution as part of a unified Dagster data platform
- Combine Spark processing with other data tools in a single orchestrated workflow
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Dagster and need to integrate Spark workloads.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and is permissively licensed. It is worth installing as a bridge between Spark and Dagster's orchestration model, though you should verify that your target Spark version is supported.
Install
dagster-spark on PyPI
Before you install
Low friction installation as a pure Python wheel with a single runtime dependency on dagster. Actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.10 or later (supports up to 3.14); Spark runtime must be available in your environment.
License in practice
Apache 2.0 licensed under a permissive model, allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install dagster-spark
import dagster as dg
from dagster_spark import spark_resource
@dg.asset
def my_spark_asset(context) -> None:
spark = context.resources.spark
# Use spark session for Spark operationsVerify before relying
- Specific Spark versions supported or tested by this integration
- Whether the package handles Spark cluster deployment or only local/existing clusters
- Performance characteristics or scaling limits for large Spark workloads
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagedagster |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 563,554 / month, #5,979 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_spark-0.29.18-py3-none-any.whl
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See also dagster-pyspark · dagster · dagster-dg-core · dagster-airbyte · dagster-aws · dagster-databricks · dagster-azure · dagster-docker · dagster-cloud-cli · dataengine