Dagster

The dg docs Component library is a collection of reusable UI components designed for two primary use cases:

1. Building the dg docs standalone documentation site.
2. Integrating documentation components directly within the Dagster application.

The dg CLI tool is used for managing Dagster environments. Its primary capabilities include:

• Entity Management: Scaffolding, checking, and listing Dagster entities.
• Pipeline Execution: Running pipelines within a local Dagster instance.
• Dagster Plus Interaction: Interacting with Dagster Plus via REST-style API commands.

Note: For REST-style API interactions, dg relies on the dagster-rest-resources library. If you need to implement a new CLI command that requires new API calls, you must first add those calls to dagster-rest-resources before calling them from dg.

Dagster is a cloud-native data pipeline orchestrator designed for the entire data development lifecycle. Its core value propositions include:

• Productivity Platform: Uses a declarative approach to identify and build data assets, supporting CI/CD best practices and reusable components.
• Orchestration Engine: A robust, multi-tenant, and multi-tool engine capable of scaling technically and organizationally.
• Unified Control Plane: Provides a centralized location for metadata, observability, diagnostics, cataloging, and lineage to manage data complexity.
• Modern Data Stack Integrations: Offers a library of integrations for popular data tools to allow deployment within existing infrastructures.

dagster-rest-resources is a library that exposes Dagster functionality as REST-style resources. It primarily works by wrapping Dagster's GraphQL API, though some resources (like artifact) may interact directly with other services like S3.

Code Organization

• api/: Contains the REST-style interface classes used by library consumers.
• queries/: Contains GraphQL queries used for internal code generation.
• schemas/: Contains Pydantic response models returned by the API classes.
• gql_client.py: Handles GraphQL calls.
• s3_client.py: Handles S3 calls.

Dagster+ AI is a suite of features designed to help users detect, understand, and resolve pipeline failures through a combination of conversational debugging, proactive monitoring, and automated code fixes.

Key capabilities include:

• Conversational Debugging: Use the AI chat to summarize run failures, identify root causes, and ask follow-up questions.
• Proactive Monitoring: The system periodically scans deployments to identify failure patterns and automatically creates Issues.
• Automated Agent Dispatch: From an existing Issue, you can dispatch an AI coding agent to generate a fix and submit it as a GitHub pull request.

Dagster+ is a managed orchestration platform built on top of the Dagster open-source engine. It manages the Dagster web UI (at https://dagster.plus), metadata stores for data cataloging and cost insights, and backend services for orchestration and alerting.

There are two primary deployment flavors:

1. Dagster+ Serverless: A fully managed offering where your Dagster code executes directly in the Dagster+ environment.
2. Dagster+ Hybrid: You run your own execution environment that connects to the Dagster+ control plane.

Both options offload the management of the orchestration control plane from your team.

Dagster is a data orchestrator designed for data engineers. It provides an integrated approach to data workflows through:

• Integrated Lineage: Visualizing how data flows through your system.
• Observability: Monitoring the health and status of your data processes.
• Declarative Programming Model: Defining what your data should look like rather than just the steps to get there.
• Testability: Built-in support for testing data pipelines and logic.

Airlift is a toolkit designed to integrate Dagster with Airflow. It is provided via the dagster-airlift package.

Key capabilities include:

• Observability: Observe Airflow instances directly from within the Dagster UI.
• Migration: Accelerate the transition of Airflow DAGs to Dagster assets using opinionated tooling.
• Federation: Use Dagster as a centralized control plane to observe DAGs from multiple Airflow instances and federate execution between them.

The project_atproto_dashboard is an end-to-end demonstration project that showcases the following workflow:

1. Data Ingestion: Ingesting data-related Bluesky posts from the ATProto API using:
   • Dynamic partitions for scalable data handling.
   • Declarative automation for orchestration.
   • Concurrency limits to respect API constraints.
2. Data Modeling: Using dbt to transform the ingested data.
3. Data Visualization: Presenting the modeled data in a Power BI dashboard.