Grafana is a highly versatile tool for visualizing data from multiple sources, such as time-series databases like Prometheus and relational databases like PostgreSQL. One of its most powerful features is the ability to build dynamic dashboards, which allow users to interact with data in real-time. These Grafana dashboards can be filtered and customized instantly, making them ideal for monitoring and analyzing data on the go.

In this guide, we’ll explore creating a dynamic dashboard in Grafana using PostgreSQL as a data source and show you how to use variables to make your data views more customizable.

Why Use Dynamic Dashboards in Grafana?

Dynamic dashboards offer several key advantages:

• Flexibility: You can filter data by various parameters (e.g., date ranges, user IDs, or server names) without creating multiple static dashboards.
• Efficiency: Instead of managing many static dashboards, one dynamic dashboard can handle multiple use cases, simplifying maintenance.
• User-Friendly: Real-time customization lets users quickly dive into the specific data they’re interested in, improving the overall experience.

Step-by-Step Guide to Creating a Dynamic Dashboard in Grafana with PostgreSQL Data

In this section, we will create a dynamic dashboard that lets users filter sales data by product category and region, using PostgreSQL as the primary data source.

Step 1: Connecting PostgreSQL to Grafana

Before building the dashboard, you must set up PostgreSQL as a data source in Grafana.

1. Go to Configuration > Data Sources in Grafana.
2. Click on Add data source.
3. Select PostgreSQL from the available options.
4. Fill out the necessary details, like Host, Database, User, and Password.
5. Click Save & Test to verify the connection.

Once connected, you can use PostgreSQL as the data source for your queries in Grafana.

Step 2: Create a New Dashboard

1. Go to the Grafana homepage and click **+** to create a new dashboard.

2. Select **Add new panel** to open the query editor.

3. Choose **PostgreSQL** as your data source.

4. Write a SQL query to fetch data. For example, to get sales data, use the following query:

    SELECT order_date, product_category, region, total_sales FROM sales_data

   WHERE product_category = 'Electronics ORDER BY order_date;

   

This query fetches the sales data for the “Electronics” category. We’ll make the product_category and region filters dynamic using Grafana’s variables.

Step 3: Making Your Grafana Dashboard Dynamic with Variables

Variables are the heart of a dynamic dashboard in Grafana. They enable users to interactively filter and adjust the data they see.

1. Click the Dashboard Settings (gear icon) at the top right.
2. Go to Variables and click Add variable.
   

Adding a Product Category Variable

1. Name the variable (e.g., product_category).

2. Set the **Type** to Query.

3. In the **Data Source** dropdown, choose **PostgreSQL**.

4. In the **Query** field, write a query to fetch distinct product categories:

   

   SELECT DISTINCT product_category FROM sales_data ORDER BY product_category;

   

5. Click **Save**. This will create a dropdown variable that lists all product categories from the sales_data table.

Adding a Region Variable:

1. Add another variable by clicking **Add variable**.

2. Name the variable (e.g., region).

3. Set **Type** to Query.

4. In the **Data Source** dropdown, select **PostgreSQL**.

5. Write the following query to fetch distinct regions:

   

   SELECT DISTINCT region FROM sales_data ORDER BY region;

   

6. Click **Save**.

Now you have two dropdown variables: one for **product categories** and one for **regions**.

Step 4: Update the SQL Query to Use Variables

Return to the query editor in your dashboard panel. Modify the SQL query to reference the newly created variables:

SELECT 

  order_date,

  product_category,

  region,

  total_sales

FROM 

  sales_data

WHERE 

  product_category = '$product_category'

  AND region = '$region'

ORDER BY 

  order_date;

Grafana will replace $product_category and $region with the values selected from the dropdown menus. This makes the dashboard dynamic, allowing users to filter the data by product category and region.

Step 5: Test and Customize the Dashboard

Now that the variables are set up, return to your dashboard. At the top, you will see dropdowns for **product category** and **region**. Select values from these dropdowns, and the data in the dashboard will update accordingly.

You can further customize the dashboard by:

– Adding visualizations: Adding visualizations: Use time-series graphs, tables, or bar charts to display the sales data. For example, if you want to display sales performance across multiple regions, you can add multiple values to a bar chart in Grafana, ensuring a comprehensive comparison.

– Adding more variables: If needed, add additional filters, such as time ranges or specific product IDs.

Benefits of Grafana Dynamic Dashboards

• Instant Filtering: With just a few clicks, quickly switch between different data sets, such as regions or product categories.
• Streamlined Data Exploration: Drill down into specific data points without writing new queries or creating new views.
• Better Decision-Making: Dynamic dashboards provide fast insights, empowering teams to make data-driven decisions more efficiently.

Add Dynamic Reporting to Dashboards with Skedler

Once your dynamic dashboard is set up, the next crucial step is automating your reporting. While Grafana dashboards provide real-time visibility, you need Skedler for managerial reporting. With Skedler, you can automate daily/weekly/monthly observability status reports for your management and clients.  Skedler offers seamless, customizable reporting for Grafana dashboards, reducing manual work and ensuring timely delivery of insights to your managers, team, or clients.

If you haven’t tried Skedler yet, download the free trial to elevate your reporting experience. Watch the full tutorial here.

The importance of filters in visualization and reporting 

Filters play a key role in data visualization, offering a strategic advantage when interpreting and presenting information. They allow users to focus on specific subsets of data, providing clarity and relevance. Filters are especially important in the following situations:

• Data complexity: Dealing with large data sets is made manageable by isolating specific parameters for analysis.
• User customization: Filters allow users to tailor reports to their specific needs, fostering a personalized analytical experience.
• Dynamic analysis: Filters facilitate dynamic adjustments of displayed data, enhancing real-time decision-making processes.

In short, filters are indispensable tools that enable users to navigate the complexities of data visualization accurately and efficiently.

When you automate managerial reports with Skedler, you can apply dynamic filters to create more tailored and insightful reports. 

For a step-by-step guide, check out our video tutorial on setting up filters in Skedler. Watch the full tutorial here

Conclusion

Dynamic dashboards in Grafana, powered by PostgreSQL, offer immense flexibility and power. By using variables, you can create a single dashboard that adapts to different queries, regions, and categories, allowing for real-time data exploration. In this example, we walked through creating a sales monitoring dashboard that adjusts based on product category and region, but these principles can be applied to any domain or use case.

Start experimenting with dynamic dashboards in Grafana today, and transform how you visualize your PostgreSQL data!  Don’t forget to save time and money by automating reports that export key visuals from your Grafana dashboards with Skedler.  

If you find this article interesting, we invite you to read our other Grafana articles, such as this one on why Skedler is the best alternative to Grafana reporting or the key differences between Grafana and Kibana.