How does Dagster differ from traditional orchestrators?

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May 2, 2024

Dagster is a data orchestrator that adopts an asset-centric approach, contrasting with traditional task-based orchestrators like Airflow. It focuses on the data assets produced, such as dbt models or tables in a data warehouse, rather than merely executing sequential tasks.

  • Asset-centric approach: Dagster models the assets you aim to create, determining the necessary steps to produce these assets. This method enhances data lineage understanding, data quality checks, and visibility into data assets.
  • Open-source: Available as an open-source tool, Dagster can be installed via pip or explored through a free trial on their cloud platform.
  • Data assets at the center: By prioritizing data assets over task execution, Dagster offers improved metadata management, data lineage, and visibility into the data products being created.

Dagster's unique asset-centric approach provides a more intuitive and effective method for data orchestration, making it a valuable tool for modern data management.

What are the key features of Dagster?

Dagster organizes and manages data workflows with a focus on the data assets produced. It features a data-aware, typed, self-describing logical orchestration graph that models the structure inherent in data applications and platforms.

  • Data-aware: The orchestration graph in Dagster is data-aware and self-describing, capturing the implicit structure of data applications.
  • Data assets: Dagster workers are designed to understand data asset dependencies, ensuring the correct execution order of tasks.
  • Incremental code development: Unlike monolithic Dags, Dagster supports incremental code development, decoupling code from production resources.
  • Software-defined assets: Introducing software-defined assets, Dagster models and persists data objects or ML models as assets in a data repository.

What are the benefits of taking an asset-centric approach with Dagster?

Adopting an asset-centric approach with Dagster offers various advantages, including enhanced data lineage visibility, simplified data quality management, and efficient policy implementation.

  • Data lineage visibility: Gain insights into data lineage, facilitating data quality management and a comprehensive data catalog.
  • Policy implementation: Implement policies like defining acceptable staleness for critical assets, with Dagster automating necessary actions to maintain data freshness.
  • Simplified management: Manage schedules based on policies, eliminating manual scheduling for different asset update frequencies.
  • Metadata integration: Seamlessly integrate metadata, monitoring, and reporting around assets for improved data management.

How does Dagster enhance the development and testing experience for data applications?

Dagster serves as a data orchestrator throughout the data development lifecycle, offering benefits for local development, testing, CI, staging, and debugging. It improves the development and testing experience for data applications through various features.

  • Data quality: Automate pipeline tasks, trigger ML model retraining, and set alerts for significant data events with Dagster.
  • Data dependency management: Explicitly define data dependencies to ensure correct execution order and data flow through pipelines.
  • Testability: Dagster's functional data processing approach enables parameterized execution and direct result verification for enhanced testability.
  • Subset execution: Easily execute subsets of graphs for testing or operational purposes with Dagster.
  • Built-in monitoring and debugging tools: Utilize Dagster's web-based dashboard for real-time pipeline performance visibility, logging, and error handling.

How does Secoda fit into a Dagster workflow?

Secoda, a data management platform powered by AI, seamlessly integrates with Dagster workflows to enhance data management, governance, and productivity.

  • Data search, catalog, lineage, monitoring, and governance: Secoda provides comprehensive features for effective data asset management.
  • Connects data quality, observability, and discovery: Secoda bridges these aspects to offer a holistic view of the data landscape.
  • Automated workflows: Enhance efficiency and productivity with Secoda's automated workflow capabilities.
  • Secoda AI: Utilize AI to connect to various data sources and tools for streamlined data access and usage.
  • Data requests portal: Simplify data access and usage with Secoda's dedicated data requests portal.
  • Automated lineage model: Gain visibility into data origins and transformations through Secoda's automated lineage model.
  • Role-based permissions: Ensure data security and governance with Secoda's role-based permission system.

Secoda seamlessly integrates with Dagster workflows, providing a centralized platform for data documentation, governance, and enhanced data management.

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