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Introduction to Dagster

Posted on:March 15, 2024

Data orchestration for a Modern Data Stack

Introduction to Dagster: A Data Orchestrator for Machine Learning and Analytics

Dagster is a powerful data orchestrator designed to streamline and optimize data processing workflows for machine learning and analytics. It provides a flexible and intuitive framework for defining, executing, and monitoring complex data pipelines. In this article, we will explore Dagster’s key concepts and how they work together to simplify data orchestration.

Software-Designed Assets

At the core of Dagster are software-designed assets, objects stored in persistent storage, such as tables, files, or persisted machine learning models. These assets are the building blocks for data processing workflows and can be easily managed and tracked within Dagster.

Ops and Graphs

Dagster introduces the concept of Ops, which are the fundamental units of computation. An Op can perform various tasks, such as:

Ops are designed to be modular and reusable, allowing for easy composition and maintenance.

Ops can be assembled into Graphs, which are sets of interconnected Ops or sub-graphs. While individual Ops typically perform simple tasks, Graphs enable the accomplishment of complex tasks by combining multiple Ops. The dependencies between Ops are expressed as data dependencies rather than just execution dependencies, enabling Dagster to optimize the execution flow based on data availability.


Jobs are the main unit of execution and monitoring in Dagster. A Job consists of a Graph of Ops connected via data dependencies. By defining the dependencies between the inputs and outputs of Ops, Dagster can efficiently execute the Job and track the flow of data throughout the pipeline.


Materialization refers to the process of creating an asset by an Op. When an Op materializes an asset, the output data persists in a specific storage location, making it available for subsequent Ops or external consumption.


Repositories serve as collections of assets, Jobs, schedules, and sensors. They can be loaded as a unit by Dagit, the web-based user interface for Dagster, and the Dagster daemon, which handles the execution of scheduled Jobs and sensors.


Workspaces in Dagster are collections of user-defined repositories along with information about where to find them. They provide a convenient way to organize and manage related repositories.


Dagster supports the scheduling of Jobs through the use of Schedules. A Schedule defines a fixed time at which a Dagster Job should be submitted for execution. This allows for automated and recurring execution of data processing pipelines.


In addition to Schedules, Dagster introduces the concept of Sensors. A Sensor is a definition that triggers the submission of a Dagster Job based on changes in the external state. For example, when data changes, indicating that an asset has been updated, Sensors can automatically trigger the execution of dependent assets without manually restarting the associated DAGs (Directed Acyclic Graphs) that are no longer scheduled.


Dagster provides support for data backfills, which allow for the processing of historical data or specific partitions of data. Backfills can be performed for each partition or subsets of partitions, enabling efficient reprocessing of data when needed.


In conclusion, Dagster offers a comprehensive set of tools and concepts for building robust and scalable data processing workflows. By leveraging software-designed assets, Ops, Graphs, Jobs, Repositories, Workspaces, Materialization, Schedules, Sensors, and Backfills, data engineers and analysts can efficiently orchestrate and manage complex data pipelines for machine learning and analytics tasks.