Single Select Option Filter Variable:
Introduction:
The visualization of plant and customer data on Grafana provides an intuitive interface for understanding the distribution and performance of various entities within a geographic context. By leveraging PostgreSQL as the backend database and Grafana as the visualization platform, this report outlines the steps involved in creating a comprehensive dashboard.
Architecture:
The architecture comprises PostgreSQL serving as the data storage solution and Grafana as the visualization tool. PostgreSQL tables store the plant and customer data, while Grafana dashboards display this information using geospatial visualizations.
Steps:
1. Create PostgreSQL Table: Begin by establishing a PostgreSQL table to store the plant and customer data securely.
2. Import Data: Import the existing data, typically in CSV format, into the PostgreSQL table to populate it with relevant information.
3. Create a Grafana Dashboard: Configure a new dashboard in Grafana, ensuring that the necessary prerequisites such as setting up geospatial visualization are in place.
4. Retrieve Data from PostgreSQL table: Craft a data layer query within Grafana to retrieve pertinent information from the PostgreSQL table. This data will be categorized into plant and customer layers for visualization.
5. Create Filter Variables: Develop dropdown menus for various filtering options such as Hub_code, Plant, Customer, Market, Sales Person, BA Structure, Circuit Name, Mineral Name, and Year. These filters enhance user interaction and facilitate data exploration.
6. Integrate Filter Variable with Data Layer: Link the filter variables with the data layer in Grafana, enabling dynamic filtering of data based on user input. This integration ensures that the visualizations respond dynamically to user selections.
7. Create Map Layers: Establish connections between the data layer and map layer within Grafana, allowing the visualization of plant and customer data on a geographical map. This step finalizes the creation of the visualization dashboard.
Final Output:
Conclusion:
In conclusion, the integration of PostgreSQL and Grafana enables the creation of dynamic and insightful dashboards for visualizing plant and customer data. By following the outlined steps, users can efficiently set up a visualization platform that offers actionable insights into geographic data distribution and performance metrics.
Multi Select Option Filter Variable:
Introduction:
The visualization of plant and customer data on Grafana provides an intuitive interface for understanding the distribution and performance of various entities within a geographic context. By leveraging PostgreSQL as the backend database and Grafana as the visualization platform, this report outlines the steps involved in creating a comprehensive dashboard.
Architecture:
The architecture comprises PostgreSQL serving as the data storage solution and Grafana as the visualization tool. PostgreSQL tables store the plant and customer data, while Grafana dashboards display this information using geospatial visualizations.
Steps:
1. Create PostgreSQL Table: Begin by establishing a PostgreSQL table to store the plant and customer data securely.
2. Import Data: Import the existing data, typically in CSV format, into the PostgreSQL table to populate it with relevant information.
3. Create a Grafana Dashboard: Configure a new dashboard in Grafana, ensuring that the necessary prerequisites such as setting up geospatial visualization are in place.
4. Retrieve Data from PostgreSQL table: Craft a data layer query within Grafana to retrieve pertinent information from the PostgreSQL table. This data will be categorized into Hub_Code, Plant, Customer, BA Structure, Market, Sales Person, Circuit Name, Commercial Name, Mineral Name, Year Name.
5. Create Filter Variables: Develop dropdown menus for various filtering options such as Hub_code, Plant, Customer, Market, Sales Person, BA Structure, Circuit Name, Mineral Name, and Year. These filters enhance user interaction and facilitate data exploration.
6. Create Map Layers: Establish connections between the data layer and map layer within Grafana, allowing the visualization of plant and customer data on a geographical map. This step finalizes the creation of the visualization dashboard.
Final Output:
