Add Dagster to ecosystem docs (#611)

Author: nickvazz

content/en/tabcontents.yaml

@@ -148,7 +148,7 @@ datascience:
   - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)"
 
   content:
-  - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org) and [Prefect](https://www.prefect.io)).
+  - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)).
 
 visualization:
   images:
@@ -186,4 +186,4 @@ visualization:
           [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), 
           and [PyVista](https://github.com/pyvista/pyvista), to name a few.
   - text: NumPy's accelerated processing of large arrays allows researchers to visualize
-          datasets far larger than native Python could handle.
+          datasets far larger than native Python could handle.