zoom_in Click to enlarge

Dagster

▲ ▼

language

description Dagster Overview

Dagster is a modern data orchestrator that shifts the focus from 'tasks' to 'assets.' Instead of just defining what code runs, Dagster helps you define what data objects are produced. This asset-centric approach makes it much easier to track data lineage and understand the state of your data platform. It provides excellent local development tools and a powerful UI for monitoring complex data pipelines.

help Dagster FAQ

How is Dagster different from Airflow?

While Apache Airflow focuses on orchestrating 'tasks' and DAGs, Dagster shifts the focus to 'assets,' defining the actual data objects produced. This asset-centric approach makes it significantly easier to track data lineage and understand data states.

What programming language is Dagster written in?

Dagster is written entirely in Python and integrates natively with modern data tools like Pandas, Spark, and dbt. This makes it highly accessible for data engineers who want to orchestrate complex pipelines using standard Python code.

Does Dagster support automatic restarts for failed pipelines?

Yes, Dagster provides robust auto-restart capabilities and retry logic for failed data assets or ops. This ensures that transient network errors don't break an entire pipeline, saving engineers time on manual troubleshooting.

What is Dagster used for in data engineering?

Dagster is used as a modern data orchestrator to define, schedule, and monitor data workflows. It is particularly popular for building ELT (Extract, Load, Transform) pipelines where tracking data lineage and testing data quality are top priorities.

swap_horiz

Looking for Dagster alternatives? Compare top competitors ranked & scored

arrow_forward