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430 lines
14 KiB
Markdown
430 lines
14 KiB
Markdown
---
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title: Data Insights
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slug: /features/data-insight
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---
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# Data Insights
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Platform adoption is an important element for teams implementing OpenMetadata. With the data insights feature organization can drive the adoption of OpenMetadata by monitoring its usage and setting up company wide KPIs.
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## Data Insight Reports
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OpenMetadata offers a suite of reports providing platform analytics around specific areas.
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### Data Assets
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The Data Assets reports display important metrics around your data assets in OpenMetadata.
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**Total Data Assets**
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This chart represents the total number of data assets present in OpenMetadata. It offers a view of your data assets broken down by asset type (i.e. Database, Table, ML Model, etc.)
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{% image
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src="/images/v1.1.0/features/data-insight/total-data-assets.png"
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alt="Total Data Assets Chart"
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caption="Total Data Assets Chart"
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/%}
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**Percentage of Data Assets with Description**
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This chart represents the percentage of data assets present in OpenMetadata with a description. For Table asset type, this condition is true only if the table and column description are filed. It allows you to quickly view the description coverage for your data assets in OpenMetadata.
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{% image
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src="/images/v1.1.0/features/data-insight/percentage-description.png"
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alt="Percentage of Assets with Description"
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caption="Percentage of Assets with Description"
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/%}
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**Percentage of Data Assets with Owners**
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This chart represents the percentage of data assets present in OpenMetadata with an owner assigned. Data assets that do not support assigning an owner will not be counted in this percentage. It allows you to quickly view the ownership coverage for your data assets in OpenMetadata.
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{% image
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src="/images/v1.1.0/features/data-insight/percentage-owner.png"
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alt="Percentage of Assets with Owner Assigned"
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caption="Percentage of Assets with Owner Assigned"
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/%}
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**Total Data Assets by Tier**
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This chart represents a broken down view of data assets by Tiers. Data Assets with no tiers assigned are not included in this. It allows you to quickly view the breakdown of data assets by tier.
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{% image
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src="/images/v1.1.0/features/data-insight/data-assets-by-tier.png"
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alt="Data Asset by Tier"
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caption="Data Asset by Tier"
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/%}
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### App Analytics
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The App Analytics report provides important metrics around the usage of OpenMetadata.
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**Most Viewed Data Assets**
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This chart shows the top 10 data assets the most viewed in your platform. It offers a quick view to understand what are the data assets with the most interest in your organization.
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{% image
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src="/images/v1.1.0/features/data-insight/most-viewed-assets.png"
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alt="Most Viewed Assets"
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caption="Most Viewed Assets"
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/%}
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**Page views by data assets**
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This chart shows the total number of page views by asset type. This allows you to understand which asset familly drives the most interest in your organization
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{% image
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src="/images/v1.1.0/features/data-insight/views-by-assets.png"
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alt="Page Views by Assets"
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caption="Page Views by Assets"
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/%}
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**Daily active users on the platform**
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This chart shows the number of daily active users on your platform. Active users are users with at least one session. This report allows to understand the platform usage and see how your organization leverage OpenMetadata.
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{% image
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src="/images/v1.1.0/features/data-insight/daily-active-users.png"
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alt="Daily Active Users"
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caption="Daily Active Users"
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/%}
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**Most Active Users**
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This chart shows the top 10 most active users. These users are your power users in your organization. They can be turned into evangelist to promote OpenMetadata inside your company.
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{% image
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src="/images/v1.1.0/features/data-insight/most-active-users.png"
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alt="Daily Active Users"
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caption="Daily Active Users"
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/%}
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### Setting up Data Insight Workflow
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**Step 1**
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Navigate to `settings > Metadata > OpenMetadata Service`.
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{% image
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src="/images/v1.1.0/features/data-insight/metadata-nav.png"
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alt="Metadata Service Page"
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caption="Metadata Service Page"
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/%}
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On the `OpenMetadata Service` click on `Add Ingestion > Add Data Insight Ingestion`
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{% image
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src="/images/v1.1.0/features/data-insight/data-insight-add-ingestion.png"
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alt="Add Data Insight Ingestion"
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caption="Add Data Insight Ingestion"
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/%}
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**Step 2**
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Pick a name for your ingestion workflow or leave it as is.
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{% image
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src="/images/v1.1.0/features/data-insight/data-insight-ingestion-name.png"
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alt="Data Insight Ingestion Name"
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caption="Data Insight Ingestion Name"
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/%}
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Add any elasticsearch configuration relevant to your setup. Note that if you are deploying OpenMetadata with no custom elasticsearch deployment you can skip this configuration step.
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{% image
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src="/images/v1.1.0/features/data-insight/data-insight-ingestion-es-config.png"
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alt="Data Insight Ingestion ES Config"
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caption="Data Insight Ingestion ES Config"
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/%}
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Choose a schedule exection time for your workflow. The schedule time is displayed in UTC. We recommend to run this workflow overnight or when activity on the platform is at its lowest to ensure accurate data.
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{% image
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src="/images/v1.1.0/features/data-insight/data-insight-ingestion-schedule.png"
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alt="Data Insight Ingestion Schedule"
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caption="Data Insight Ingestion Schedule"
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/%}
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**Step 3**
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Navigate to the `Insights` page. You should see your data insights reports. Note that if you have just deployed OpenMetadata, `App Analytic` data might not be present. `App Analytic` data are fetched from the previous day (UTC).
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## Data Insight KPIs
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While data insights reports gives an analytical view of OpenMetadata platform, KPIs are here to drive platform adoption.
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{% image
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src="/images/v1.1.0/features/data-insight/data-insight-kpi.png"
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alt="Data Insight KPI"
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caption="Data Insight KPI"
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/%}
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### KPIs Categories
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**Completed Description**
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Available as an absolute or relative (percentage) value, this KPI measures the description coverage of your data assets in OpenMetadata.
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**Completed Ownership**
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Available as an absolute or relative (percentage) value, this KPI measures the ownershi[] coverage of your data assets in OpenMetadata.
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### Adding KPIs
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On the `Insights` page, click on `Add KPI`. This will open the KPI configuration page where the following required configuration elements need to be set:
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- `Name`: name of your KPI
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- `Select a chart`: this links the KPI to one of the chart present in the data insight reports
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- `Select a metric type`: you can choose between `PERCENTAGE` or `NUMBER`. The former will be a relative value while the latter an absolute value
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- `Start date` / `End date`: this will determine the start and end date of your KPI. It sets an objective for your organization
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{% image
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src="/images/v1.1.0/features/data-insight/configure-kpi.png"
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alt="KPI Configuration"
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caption="KPI Configuration"
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/%}
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# Run Data Insights using the Airflow SDK
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### 1. Define the YAML Config
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This is a sample config for Data Insights:
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```yaml
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source:
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type: dataInsight
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serviceName: OpenMetadata
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sourceConfig:
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config:
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type: MetadataToElasticSearch
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processor:
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type: data-insight-processor
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config: {}
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sink:
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type: elasticsearch
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config:
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es_host: localhost
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es_port: 9200
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recreate_indexes: false
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workflowConfig:
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loggerLevel: DEBUG
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openMetadataServerConfig:
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hostPort: "<OpenMetadata host and port>"
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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#### Source Configuration - Source Config
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- To send the metadata to OpenMetadata, it needs to be specified as `type: MetadataToElasticSearch`.
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#### processor Configuration
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- To send the metadata to OpenMetadata, it needs to be specified as `type: data-insight-processor`.
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#### Workflow Configuration
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The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
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For a simple, local installation using our docker containers, this looks like:
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
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You can find the different implementation of the ingestion below.
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### 2. Prepare the Data Insights DAG
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Create a Python file in your Airflow DAGs directory with the following contents:
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```python
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import pathlib
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import yaml
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from datetime import timedelta
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from airflow import DAG
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from metadata.data_insight.api.workflow import DataInsightWorkflow
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try:
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from airflow.operators.python import PythonOperator
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except ModuleNotFoundError:
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from airflow.operators.python_operator import PythonOperator
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from metadata.config.common import load_config_file
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from airflow.utils.dates import days_ago
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default_args = {
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"owner": "user_name",
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"email": ["username@org.com"],
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"email_on_failure": False,
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"retries": 3,
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"retry_delay": timedelta(minutes=5),
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"execution_timeout": timedelta(minutes=60)
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}
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config = """
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<your YAML configuration>
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"""
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def metadata_ingestion_workflow():
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workflow_config = yaml.safe_load(config)
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workflow = DataInsightWorkflow.create(workflow_config)
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workflow.execute()
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workflow.raise_from_status()
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workflow.print_status()
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workflow.stop()
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with DAG(
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"sample_data",
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default_args=default_args,
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description="An example DAG which runs a OpenMetadata ingestion workflow",
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start_date=days_ago(1),
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is_paused_upon_creation=False,
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schedule_interval='*/5 * * * *',
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catchup=False,
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) as dag:
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ingest_task = PythonOperator(
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task_id="ingest_using_recipe",
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python_callable=metadata_ingestion_workflow,
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)
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```
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# Run Data Insights using the metadata CLI
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### 1. Define the YAML Config
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This is a sample config for Data Insights:
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```yaml
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source:
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type: dataInsight
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serviceName: OpenMetadata
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sourceConfig:
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config:
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type: MetadataToElasticSearch
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processor:
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type: data-insight-processor
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config: {}
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sink:
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type: elasticsearch
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config:
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es_host: localhost
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es_port: 9200
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recreate_indexes: false
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workflowConfig:
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loggerLevel: DEBUG
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openMetadataServerConfig:
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hostPort: "<OpenMetadata host and port>"
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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#### Source Configuration - Source Config
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- To send the metadata to OpenMetadata, it needs to be specified as `type: MetadataToElasticSearch`.
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#### processor Configuration
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- To send the metadata to OpenMetadata, it needs to be specified as `type: data-insight-processor`.
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#### Workflow Configuration
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The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
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For a simple, local installation using our docker containers, this looks like:
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
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You can find the different implementation of the ingestion below.
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### 2. Run with the CLI
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First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
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```bash
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metadata insight -c <path-to-yaml>
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```
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# Run Elasticsearch Reindex using the Airflow SDK
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### 1. Define the YAML Config
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This is a sample config for Elasticsearch Reindex:
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```yaml
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source:
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source:
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type: metadata_elasticsearch
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serviceName: openMetadata
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serviceConnection:
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config:
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type: MetadataES
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sourceConfig:
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config: {}
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sink:
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type: elasticsearch
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config:
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es_host: localhost
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es_port: 9200
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recreate_indexes: true
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workflowConfig:
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openMetadataServerConfig:
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hostPort: http://localhost:8585/api
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authProvider: openmetadata
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securityConfig:
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jwtToken: "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
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```
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### 2. Prepare the Ingestion DAG
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Create a Python file in your Airflow DAGs directory with the following contents:
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```python
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import pathlib
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import yaml
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from datetime import timedelta
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from airflow import DAG
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try:
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from airflow.operators.python import PythonOperator
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except ModuleNotFoundError:
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from airflow.operators.python_operator import PythonOperator
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from metadata.config.common import load_config_file
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from metadata.ingestion.api.workflow import Workflow
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from airflow.utils.dates import days_ago
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default_args = {
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"owner": "user_name",
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"email": ["username@org.com"],
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"email_on_failure": False,
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"retries": 3,
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"retry_delay": timedelta(minutes=5),
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"execution_timeout": timedelta(minutes=60)
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}
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config = """
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<your YAML configuration>
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"""
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def metadata_ingestion_workflow():
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workflow_config = yaml.safe_load(config)
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workflow = Workflow.create(workflow_config)
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workflow.execute()
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workflow.raise_from_status()
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workflow.print_status()
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workflow.stop()
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with DAG(
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"sample_data",
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default_args=default_args,
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description="An example DAG which runs a OpenMetadata ingestion workflow",
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start_date=days_ago(1),
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is_paused_upon_creation=False,
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schedule_interval='*/5 * * * *',
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catchup=False,
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) as dag:
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ingest_task = PythonOperator(
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task_id="ingest_using_recipe",
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python_callable=metadata_ingestion_workflow,
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)
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```
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