---
id: dagster-embedded-elt
version: "0.29.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# dagster-embedded-elt — Package for performing ETL/ELT tasks with Dagster.

License: permissive · Maintenance: active · Downloads: 92.0K/mo

## 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 above — verify before relying.

Provides ETL/ELT integration for Dagster, enabling data pipeline orchestration with embedded extraction and loading capabilities through dagster-dlt and dagster-sling connectors.

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

pip install dagster-embedded-elt
uv add dagster-embedded-elt
poetry add dagster-embedded-elt

## Installing dagster-embedded-elt

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.

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
    pass

Requires Python 3.10 or later (supports up to 3.14); depends on dagster, dagster-dlt, and dagster-sling runtime packages.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 92.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dagster etl elt integration, data pipeline orchestration, embedded extraction loading, dagster data connectors, etl task automation, data asset orchestration, pipeline scheduling and monitoring, data-orchestration, etl-elt, pipeline-automation

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