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Dagster Fundamentals

Dagster is an open-source Python framework and orchestration platform that builds, schedules, and observes data pipelines around the data they produce, such as tables, files, and machine learning models, rather than around the individual steps that produce them.

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Dagster Fundamentals

Dagster is an open-source Python framework and orchestration platform for building, scheduling, and observing data pipelines. It models a pipeline as a graph of assets — the tables, files, models, and reports a pipeline produces — instead of a graph of tasks that happen to run in some order.

The central mental model is you declare what should exist, and Dagster figures out how and when to build it.

Python function -> @asset -> AssetKey + dependencies -> asset graph
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                            materialization run
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                     stored output + tracked lineage

An asset represents a logical unit of data such as a table, dataset, or machine learning model. You write a Python function that computes it, and Dagster tracks the function, its declared upstream dependencies, and every time it runs.

Why an asset-based model exists

A data platform is usually described by the outputs people actually care about: a customer table, a daily report, a trained model. A task-based orchestrator asks you to describe the steps that produce those outputs — extract, transform, load — and leaves the mapping from steps to data implicit in the code that connects them.

That gap creates real operational costs:

Which table does a failed step actually affect?
Is this table stale, and which upstream table caused it?
What is the full lineage from raw source to this report?
Can someone browse the data platform without reading pipeline code?

Dagster's asset graph answers these questions directly, because the object you materialize — a table — and the object you schedule and observe — an asset — are the same object.

An asset is a Python function plus a key

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