{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks"},{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring"}],"enrichment":{"capability":"Dagster is a data pipeline orchestrator that lets you declare data assets as Python functions and automatically runs them at the right time to keep those assets up-to-date, with built-in lineage tracking and observability.","skillfed_tags":["data-orchestration","asset-management","etl"],"use_cases":["Build and schedule ETL pipelines that transform raw data into analytics-ready tables, with automatic dependency resolution and failure handling.","Develop machine learning workflows where training datasets, model artifacts, and predictions are tracked as versioned assets with full lineage.","Monitor data quality and catch upstream issues early by defining asset dependencies and running tests at each stage of the pipeline.","Orchestrate complex multi-step analytics reports that depend on multiple upstream data sources and need to run on a schedule.","Manage data assets across teams in a centralized control plane with observability, cataloging, and role-based access.","Test data pipelines locally during development, then promote the same code to staging and production without rewriting."],"what_it_does":"Dagster is a cloud-native orchestration platform designed to manage the full lifecycle of data assets\u2014tables, datasets, machine learning models, reports\u2014from local development through production. You define assets as Python functions decorated with @dg.asset, declare their dependencies, and Dagster handles scheduling, execution, and keeping them up-to-date. It provides a declarative programming model, integrated lineage tracking, and observability built in, so you can see what data flows where and spot issues early.\n\nThe platform scales from solo development (with local testing and unit test support) to production deployments with multi-tenant orchestration, centralized metadata management, and diagnostics. It ships with a web UI for monitoring and a CLI for management, and integrates with popular data tools across the modern data stack. The fact sheet shows active maintenance (released 7 days ago), production-stable status, and a large runtime dependency footprint (28 packages) that is already bundled, so installation is straightforward.","worth_installing":"Yes. Dagster is a mature, actively maintained orchestration platform (released 7 days ago, production-stable) with low install friction, permissive licensing, and no known vulnerabilities. It is well-suited for teams building data pipelines of any scale, from local development to production. The large dependency footprint is pre-packaged, so installation is straightforward. Install it if you need declarative asset-based orchestration with integrated lineage and observability; skip it if you prefer lightweight task scheduling or are committed to a different orchestration paradigm."},"id":"dagster","links":{"html":"https://skillfed.io/packages/dagster","md":"https://skillfed.io/packages/dagster.md","pypi":"https://pypi.org/project/dagster/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dagster","python_support":"supports_current","summary":"Dagster is an orchestration platform for the development, production, and observation of data assets."},"popularity":{"monthly_downloads":9669960,"position":1508,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.13.17"}