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.
