1. Cloud-Native Approach: Astronomer primarily focuses on providing cloud-native Apache Airflow deployments. It offers a managed platform that automates the deployment, management, and scaling of Airflow instances on cloud providers like AWS, Azure, and Google Cloud Platform. On the other hand, Dagster is a more general-purpose data orchestrator that is cloud-agnostic and can be deployed on any infrastructure.

2. Data Flow Paradigm: Astronomer is built around the concept of Directed Acyclic Graphs (DAGs), where tasks are represented as nodes and dependencies are represented as edges. It follows a more traditional ETL (Extract, Transform, Load) data flow paradigm. In contrast, Dagster uses a data-centric approach where data itself is the primary abstraction, and data transformations are defined as solid functions. This allows for a more modular and composable design of data pipelines.

3. Developer Workflow: Astronomer focuses on providing a user-friendly UI and a low-code development experience. It offers a graphical interface for designing DAGs and managing pipeline configurations. Dagster, on the other hand, is designed to be developer-centric and offers a powerful programming model. Developers can define their pipelines in Python code using the Dagster API, which provides fine-grained control and flexibility in pipeline construction.

4. Monitoring and Observability: Astronomer provides a built-in monitoring and observability solution for Airflow deployments. It offers visibility into task statuses, execution logs, and metrics through its UI, making it easy to monitor pipeline performance. Dagster, on the other hand, does not provide a native monitoring solution. However, it integrates well with other monitoring tools and frameworks, allowing users to leverage their preferred monitoring stack.

5. Built-in Operators and Sensors: Astronomer comes with a wide range of built-in operators and sensors that are commonly used in data workflows. These operators provide out-of-the-box functionality for common tasks like data extraction, transformation, and loading. Dagster, on the other hand, does not provide a built-in library of operators. Instead, it focuses on providing a framework for building custom solid functions that can be composed to create complex data transformations.

6. Community and Ecosystem: Astronomer has a large and active community of users and contributors. It has a marketplace where users can discover and share reusable DAGs and integrations. In contrast, Dagster is a relatively newer framework with a smaller but growing community. While it may have a smaller ecosystem currently, it offers strong extensibility through its Python API, allowing users to integrate with existing tools and libraries.

In Summary, Astronomer is a cloud-native platform focused on managing Apache Airflow deployments with a graphical UI, while Dagster is a cloud-agnostic data orchestrator with a developer-centric programming model.