dagster-embedded-elt
Package for performing ETL/ELT tasks with Dagster.
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster for data orchestration and need to add extraction and loading capabilities. The package has low install friction, active maintenance, a permissive license, and no known vulnerabilities. It is most valuable as part of a broader Dagster deployment; installing it in isolation without the core Dagster ecosystem provides limited benefit.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); depends on dagster, dagster-dlt, and dagster-sling runtime packages.
- Low install friction with a pure-Python wheel distribution.
- The package is actively maintained with recent commits and sits within Dagster's established ecosystem, which has 15997 GitHub stars and receives regular updates.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for enterprise data pipelines.
last release 2026-04-17 (119 days) · last repo commit 2026-08-14 · 15,997 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,023 downloads/mo, #13,488 on PyPI
Alternatives
Verify before relying
pip install dagster-embedded-elt
import dagster as dg
from dagster_embedded_elt import ...
@dg.asset
def my_data_asset():
# Define your ETL/ELT task
pass- Specific capabilities and API surface of dagster-dlt and dagster-sling integrations not detailed in excerpt
- Whether this package is a thin wrapper or adds substantial orchestration logic beyond its dependencies
- Performance characteristics and scalability limits for production data volumes
What it is and what it does
dagster-embedded-elt is a specialized integration package that extends Dagster's data orchestration capabilities with built-in ETL/ELT (extract-transform-load / extract-load-transform) connectors. It sits on top of three runtime dependencies—dagster (the core orchestrator), dagster-dlt, and dagster-sling—to provide a unified interface for defining and running data extraction and loading tasks as part of Dagster's asset-based pipeline model.
The package is designed for teams building data pipelines who want to declare their extraction and loading logic as Python functions (Dagster assets) and have them orchestrated alongside transformations and other data operations. It inherits Dagster's declarative programming model, integrated lineage tracking, and observability features, allowing you to develop and test pipelines locally before promoting them to staging and production environments.
Use it for
- Define ETL pipelines as Dagster assets and orchestrate them with built-in scheduling and monitoring.
- Integrate external data sources via dlt or Sling connectors while maintaining lineage and data quality checks.
- Build multi-stage data workflows combining extraction, transformation, and loading in a single declarative graph.
- Automate recurring data ingestion tasks with Dagster's scheduling and dependency resolution.
- Monitor and troubleshoot data pipeline failures with Dagster's observability and diagnostics tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Dagster for data orchestration and need to add extraction and loading capabilities.
The package has low install friction, active maintenance, a permissive license, and no known vulnerabilities. It is most valuable as part of a broader Dagster deployment; installing it in isolation without the core Dagster ecosystem provides limited benefit.
Install
dagster-embedded-elt on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. The package is actively maintained with recent commits and sits within Dagster's established ecosystem, which has 15997 GitHub stars and receives regular updates.
Requires Python 3.10 or later (supports up to 3.14); depends on dagster, dagster-dlt, and dagster-sling runtime packages.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for enterprise data pipelines.
Quickstart
pip install dagster-embedded-elt
import dagster as dg
from dagster_embedded_elt import ...
@dg.asset
def my_data_asset():
# Define your ETL/ELT task
passVerify before relying
- Specific capabilities and API surface of dagster-dlt and dagster-sling integrations not detailed in excerpt
- Whether this package is a thin wrapper or adds substantial orchestration logic beyond its dependencies
- Performance characteristics and scalability limits for production data volumes
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-dltdagster-slingdagster |
| Maintenance | Actively maintained 119 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,023 / month, #13,488 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: dagster_embedded_elt-0.29.1-py3-none-any.whl
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See also dagster-dlt · dagster-sling · meltano · dagster · dagster-airbyte · dagster-aws · dagster-cloud-cli · dagster-graphql · dagster-webserver · dagster-rest-resources