Interactive Queries offers free-form Structured Query Language (SQL) capabilities for advanced analytics. It lets you create customized data views and graphs for in-depth data analysis. With comprehensive SQL access, you can directly connect to cleansed, processed, and enriched data within your data model. This enables seamless exploration and analysis without the need for additional setup or maintenance. SQL proficiency is necessary.

Capabilities

The following are the capabilities of Interactive Queries:

• SQL Queries: Create and reuse query snippets directly in the SQL editor.

• Data Exploration: Browse your data model schema and insert schema elements into the SQL editor with a simple click-to-insert feature.

• Dashboard Creation: Create interactive dashboards with drag-and-drop functionality to resize and arrange visualizations derived from query results.

• Visualization: Use a wide range of visualization types including charts, graphs, and tables.

• Scheduled Reports: Schedule and automate report generation.

• Alerts and Notifications: Get alerts and notifications based on predefined conditions.

• Data Export Connectivity: Export SQL query results to major cloud services such as Amazon S3, Google Cloud Storage (GCS) or Azure Blob. For more information, refer Interactive Queries Unload.

• Data Validation: Validate data as it enters CDP using the zone_datavalidation.dw schema for API integrations and service users.

  • zone_datavalidation.dw_realtime: Used to validate data in real-time scenarios, such as troubleshooting web tags or expanding existing data points or types.

  • zone_datavalidation.dw_batch: Used to validate data from source system integrations to confirm that newly added data points flow correctly into CDP.

Use cases

The following are the use cases of Interactive Queries:

• Data analysts and scientists can navigate the data model schema and select tables or fields to drop into the SQL query editor to create views. They can seamlessly transit these insights into interactive dashboards, utilizing various visualization types such as charts, graphs, and tables to effectively present data insights. Additionally, they can automate reports and notification alerts to streamline reporting across teams.

• Data analysts and scientists can seamlessly export SQL query results directly to major cloud services such as Amazon S3, Google Cloud Storage (GCS), or Azure Blob, facilitating smooth data integration and sharing.

Interactive Queries

Interactive Queries offers free-form Structured Query Language (SQL) capabilities for advanced analytics. It lets you create customized data views and graphs for in-depth data analysis. With comprehensive SQL access, you can directly connect to cleansed, processed, and enriched data within your data model. This enables seamless exploration and analysis without the need for additional setup or maintenance. SQL proficiency is necessary.

Capabilities

The following are the capabilities of Interactive Queries:

• SQL Queries: Create and reuse query snippets directly in the SQL editor.

• Data Exploration: Browse your data model schema and insert schema elements into the SQL editor with a simple click-to-insert feature.

• Dashboard Creation: Create interactive dashboards with drag-and-drop functionality to resize and arrange visualizations derived from query results.

• Visualization: Use a wide range of visualization types including charts, graphs, and tables.

• Scheduled Reports: Schedule and automate report generation.

• Alerts and Notifications: Get alerts and notifications based on predefined conditions.

• Data Export Connectivity: Export SQL query results to major cloud services such as Amazon S3, Google Cloud Storage (GCS) or Azure Blob. For more information, refer Interactive Queries Unload.

• Data Validation: Validate data as it enters CDP using the zone_datavalidation.dw schema for API integrations and service users.

  • zone_datavalidation.dw_realtime: Used to validate data in real-time scenarios, such as troubleshooting web tags or expanding existing data points or types.

  • zone_datavalidation.dw_batch: Used to validate data from source system integrations to confirm that newly added data points flow correctly into CDP.

Use cases

The following are the use cases of Interactive Queries:

• Data analysts and scientists can navigate the data model schema and select tables or fields to drop into the SQL query editor to create views. They can seamlessly transit these insights into interactive dashboards, utilizing various visualization types such as charts, graphs, and tables to effectively present data insights. Additionally, they can automate reports and notification alerts to streamline reporting across teams.

• Data analysts and scientists can seamlessly export SQL query results directly to major cloud services such as Amazon S3, Google Cloud Storage (GCS), or Azure Blob, facilitating smooth data integration and sharing.