dagster-pyspark
Package for PySpark Dagster framework components.
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster and need to run PySpark workloads. It is actively maintained, has no known vulnerabilities, installs with low friction, and carries a permissive license. Install it only if you have Spark jobs to orchestrate; it adds no value as a standalone package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and a working PySpark installation (pyspark is a runtime dependency).
- Low friction install with a pure-Python wheel.
- Dagster itself is actively maintained with recent releases and strong community engagement.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary contexts.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 632,109 downloads/mo, #5,655 on PyPI
Alternatives
Verify before relying
pip install dagster-pyspark
import dagster as dg
from dagster_pyspark import pyspark_resource
@dg.asset
def my_spark_asset() -> None:
pass- Whether this package provides Spark-specific resource types or ops beyond what base Dagster offers
- Whether it includes built-in support for Spark SQL, DataFrame operations, or just job submission
- Performance characteristics and scalability limits when orchestrating large Spark clusters
What it is and what it does
Dagster-pyspark is a Dagster integration package that brings PySpark into Dagster's asset-oriented orchestration model. It provides components to define data assets that run on Spark, allowing you to declare transformations as Python functions while leveraging Spark's distributed computing. The package sits between Dagster (the orchestrator) and PySpark (the execution engine), handling resource configuration and job submission.
You use it when you have Spark workloads you want to orchestrate alongside other data assets in Dagster. It depends on dagster and pyspark as runtime dependencies, so you need both installed. The package is part of the broader Dagster ecosystem and is actively maintained alongside the main framework.
Use it for
- Orchestrate Spark jobs as part of a larger Dagster asset graph with lineage tracking and observability.
- Define reusable Spark transformations as Dagster assets that can be tested and versioned in code.
- Integrate Spark-based ETL workloads with non-Spark data tasks in a single declarative pipeline.
- Deploy Spark jobs to production with Dagster's multi-tenant orchestration engine and monitoring.
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 run PySpark workloads.
It is actively maintained, has no known vulnerabilities, installs with low friction, and carries a permissive license. Install it only if you have Spark jobs to orchestrate; it adds no value as a standalone package.
Install
dagster-pyspark on PyPI
Before you install
Low friction install with a pure-Python wheel. Dagster itself is actively maintained with recent releases and strong community engagement. Requires Python 3.10 or later.
Requires Python 3.10 or later and a working PySpark installation (pyspark is a runtime dependency).
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary contexts.
Quickstart
pip install dagster-pyspark
import dagster as dg
from dagster_pyspark import pyspark_resource
@dg.asset
def my_spark_asset() -> None:
passVerify before relying
- Whether this package provides Spark-specific resource types or ops beyond what base Dagster offers
- Whether it includes built-in support for Spark SQL, DataFrame operations, or just job submission
- Performance characteristics and scalability limits when orchestrating large Spark clusters
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 | 3 packagesdagster-sparkdagsterpyspark |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 632,109 / month, #5,655 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_pyspark-0.29.18-py3-none-any.whl
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See also dagster-spark · dagster-pandera · dagster · dagster-fivetran · dagster-dg-core · dagster-databricks · dagster-dbt · dagster-cloud-cli · dagster-webserver · dagster-snowflake-pandas