docs/api/tutorials/dashboard-chart.md
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The dashboard and chart entities are used to represent visualizations of data, typically in the context of business intelligence or analytics platforms. They allow users to create, manage, and share visual representations of data insights.
This guide will show you how to
For this tutorial, you need to deploy DataHub Quickstart and ingest sample data. For detailed steps, please refer to DataHub Quickstart Guide.
{{ inline /metadata-ingestion/examples/library/chart_create_simple.py show_path_as_comment }}
You can associate datasets with the chart by providing the dataset URN in the input_datasets parameter. This will create lineage between the chart and the datasets, so you can track the data sources used by the chart.
{{ inline /metadata-ingestion/examples/library/chart_create_complex.py show_path_as_comment }}
{{ inline /metadata-ingestion/examples/library/dashboard_create_simple.py show_path_as_comment }}
You can associate charts, dashboards, and datasets with the dashboard by providing their URNs in the charts, dashboards, and input_datasets parameters, respectively. This will create lineage between the dashboard and the associated entities.
{{ inline /metadata-ingestion/examples/library/dashboard_create_complex.py show_path_as_comment }}
{{ inline /metadata-ingestion/examples/library/chart_read.py show_path_as_comment }}
>> Chart name: example_chart
>> Chart platform: urn:li:dataPlatform:looker
>> Chart description: looker chart for production
{{ inline /metadata-ingestion/examples/library/dashboard_read.py show_path_as_comment }}
>> Dashboard name: example_dashboard
>> Dashboard platform: urn:li:dataPlatform:looker
>> Dashboard description: looker dashboard for production