In this article, I'll go overusing Cube and D3.js, the most well-liked data visualisation package, to create a straightforward dashboard application. Although Cube doesn't come with a visualisation layer by itself, integrating it with any existing charting library is quite simple.

The dashboard's online demo is available here, and the example app's complete source code is accessible on Github.

D3 (Data-Driven Documents) is a JavaScript library that is commonly used to create dynamic and interactive data visualizations on the web. A D3 dashboard is a web-based dashboard that uses D3 to display data visualizations that are updated in real-time.

The working of a D3 dashboard can be broken down into the following steps:

Data collection and preprocessing: The first step is to collect and preprocess the data that will be used to generate the visualizations. This may involve cleaning and transforming the data and preparing it in a format that can be easily consumed by D3.
Designing the layout: Once the data has been collected, the next step is to design the layout of the dashboard. This involves deciding what visualizations to include, how they will be arranged on the page, and how they will interact with each other.
Creating the visualizations: With the layout in place, the next step is to create the individual visualizations using D3. This may involve using a variety of D3 functions and components, such as scales, axes, and shapes, to create the desired visualizations.
Adding interactivity: To make the dashboard more useful and engaging, interactivity can be added to the visualizations. This may involve adding hover effects, click events, and other types of user interactions.
Real-time updates: Finally, to make the dashboard truly dynamic, real-time updates can be added to the visualizations. This can be achieved using technologies such as WebSockets or long-polling, which allow the dashboard to receive updates from a server and update the visualizations in real-time.

Overall, a D3 dashboard is a powerful tool for displaying data in an interactive and dynamic way, and can be used for a wide range of applications, from business analytics to scientific research.
Data collection and preprocessing: Data for a D3 dashboard can come from a variety of sources, including databases, APIs, and CSV files. Before the data can be visualized, it often needs to be cleaned and transformed. This can involve removing invalid or missing data points, converting data types, and aggregating data into the desired format.
Designing the layout: The layout of a D3 dashboard is typically divided into several sections, each containing one or more visualizations. The layout can be designed using HTML, CSS, and JavaScript, with D3 used to create the individual visualizations. The layout should be easy to navigate, with clear labels and a logical flow.
Creating the visualizations: D3 provides a wide range of functions and components for creating visualizations, including bar charts, line charts, scatter plots, and heatmaps. These visualizations can be customized with a range of parameters, such as color, size, and shape. D3 also provides tools for handling data scales and axes, which are important for accurately representing data.
Adding interactivity: Interactivity can be added to D3 visualizations using JavaScript event handlers, such as mouseover, click, and zoom. This can allow users to interact with the visualizations and explore the data in more detail. Tooltips and labels can also be added to provide additional information.
Real-time updates: To make a D3 dashboard truly dynamic, it should be able to receive real-time updates from a server. This can be achieved using technologies such as WebSockets or long-polling, which allow the dashboard to receive updates as soon as new data is available. D3 can then be used to update the visualizations in real-time, providing a live view of the data.
D3.js: D3 is a powerful JavaScript library for data visualization that provides a wide range of tools for creating interactive and dynamic visualizations. D3 is well-suited for creating complex and custom visualizations that are tailored to the needs of a specific project.
Responsive design: A key feature of a D3 dashboard is its ability to be responsive to different screen sizes and devices. This can be achieved using CSS media queries and JavaScript events to adjust the layout and visualizations based on the screen size. A responsive design ensures that the dashboard is usable and accessible on a wide range of devices, from desktops to smartphones.

Performance: Performance is a key consideration when creating a D3 dashboard, especially when dealing with large or complex data sets. To ensure good performance, it's important to optimize the code, minimize the use of animations and transitions, and use data aggregation and sampling where appropriate. It's also important to test the dashboard on a range of devices and browsers to ensure that it performs well in all scenarios.
Accessibility: Another important consideration when creating a D3 dashboard is accessibility. The dashboard should be designed to be usable by people with disabilities, including those with visual or motor impairments. This can involve using appropriate color contrast, providing alternative text for visual elements, and ensuring that the dashboard is navigable using keyboard-only controls.
Integration with other technologies: A D3 dashboard can be integrated with a range of other technologies to provide additional functionality. This can include server-side technologies such as Node.js, database technologies such as MongoDB or MySQL, and front-end frameworks such as React or Angular. Integration with other technologies can help to enhance the functionality of the dashboard and make it more flexible and scalable.

Setting up a Database and Cube:

A database is the first thing that we need to set up. For this article, Postgres will be utilized as the database. You can use your preferred SQL (or Mongo) database, though. To connect to various databases, please consult the Cube documentation.

You can import our sample e-commerce Postgres dataset if you don't already have any data for the dashboard.

We can now establish the Cube API service because we have data in the database. To create a new service set up to work with a Postgres database, type the following command into your terminal:

$ npx cubejs-cli create d3-dashboard -d postgres

Environment variables are used by Cube to configure itself. The database type and name must be entered to configure the connection to our database. Replace the information in the.env file in the Cube project folder with the following:

CUBEJS_API_SECRET=SECRET
CUBEJS_DB_TYPE=postgres
CUBEJS_DB_NAME=ecom
CUBEJS_WEB_SOCKETS=true
CUBEJS_DEV_MODE=true

Making a Cube data schema is the next stage. The SQL code generated by Cube from the data schema will be run in your database. Based on the database's tables, Cube Playground may produce straightforward schemas. Let's go to the Schema page and create the schemas our dashboard requires. Click Generate Schema after selecting the line items, orders, products, product categories, and users tables.

Let's evaluate our recently created schema. Select a measure from the dropdown menu on the Build page. You ought to be able to view a straightforward line graph. To view a D3 visualisation sample, select D3 from the charting library dropdown menu. Keep in mind that it's only an example and that you may always expand and customise it.

Let's change our schema right away. Although the schema creation makes it simple to get started and test the dataset, we nearly always need to make manual adjustments for real-world use cases. You can move on to the following chapter, where we'll concentrate on rendering outcomes using D3, by skipping this optional step.

We specify metrics and dimensions in the schema along with how they correspond to SQL queries. Here, you can find a wealth of information regarding data schema. To the Orders cube, we're going to add a priceRange dimension. It will say whether the order's total price fits into one of the following categories: "$0 - $100," "$100 - $200," or "$200+."

We must first create a pricing dimension for the order to accomplish this. Orders don't have a price field in our database, but we can figure it out using the line items' combined prices. The Orders and LineTimes cubes already have a relationship that has been automatically identified and defined by our schema. There is additional information about joining here.

// You can check the belongsTo join
// to the Orders cube inside the LineItems cube
joins: {
 Orders: {
 sql: `${CUBE}. order_id = ${Orders}.id`,
 relationship: `belongsTo`
 }
}

Price measure with a sum type can be found in the LineItems cube. The total of all the line items that are a part of that order will be provided if we use this measure from the Orders cube as a dimension. A subQuery dimension is what it is known as; you can read more about it here.

We can now build a priceRange dimension based on this dimension. To describe conditional logic for our price buckets, we will use a case statement.

// Add the following dimension to the Orders cube
priceRange: {
 type: `string`,
 case: {
 when: [
 { sql: `${price} < 101`, label: `$0 - $100` },
 { sql: `${price} < 201`, label: `$100 - $200` }
 ],
 else: {
 label: `$200+`
 }
 }
}

Let's test out our brand-new dimension! Select the Orders count measure along with the Orders price range dimension on the Build tab in the playground. The generated SQL can always be verified by selecting the SQL button on the control panel.

The API portion is now complete. We'll take a deeper look at using D3 to render the outcomes of our queries in the following chapter.

Rendering Chart with D3.js:

Let's examine the example code playground uses to display our first chart with the D3 now that we have built it. Prior to that, it's important to comprehend how Cube receives, processes, and gives the results of a query.

A Cube query is a straightforward JSON object with numerous attributes. The query's measures, dimensions, timeDimensions, and filters make up its primary properties. More information on the Cube JSON query style and its attributes can be found here. By selecting the JSON Query button next to the chart selector, you can always view the JSON query in the sandbox.

This question is accepted by Cube API, which then generates a SQL query using it and the earlier-created schema. The customer will receive the outcome of this SQL query after it has been processed in our database.

Although the Cube JavaScript client library is available, we'll still use the standard HTTP REST API to query the database. It offers helpful tools to handle the data after it has been returned from the API, among other things.

The Cube client generates a ResultSet object, which offers several methods for accessing and manipulating the data, after the data has been loaded. Two of them, ResultSet.series and ResultSet.chartPivot, will be used right away. The documentation for the Cube client library contains information on every function.

The array of data series with key, title, and series data is the output of the ResultSet.series function. PivotConfig, the only parameter accepted by the method. We'll speak a little bit about it; it is an object that contains guidelines for how the data should be pivotalized. Each series is typically depicted by a different line in a line graph. Using this technique, data can be prepared in the manner required by D3.

As was already stated, the pivotConfig argument is an object that controls the transformation or pivoting of data. The object has two array-based attributes, x and y. You can choose what goes to the X-axis and what goes to the Y-axis by adding measures or measurements to one of them. The default setting of pivotConfig for a query with one measure and one timeDimension is as follows:


{
 x: `CubeName.myTimeDimension. granularity`,
 y: `measures`
}

Since "measures" is a special number in this context, all measures should be plotted on the Y-axis. The pivotConfig's preset setting ought to be adequate in many situations. I'll demonstrate when and how we should alter it in the following chapter.

Let's now examine the frontend code that the sandbox creates when we choose a D3 chart. Change the visualisation style to D3 and choose a measure in the playground. Next, select Code to view the front-end code used to display the graphic.

D3 dashboard refers to a web-based data visualization dashboard created using D3.js, a JavaScript library for creating dynamic and interactive data visualizations in web browsers.

With D3.js, developers can create a variety of visualizations, including bar charts, line charts, pie charts, maps, and more, using HTML, SVG, and CSS. These visualizations can then be combined and arranged in a dashboard format to provide an overview of key metrics, trends, and insights.

D3 dashboards can be customized to display real-time data, allow user interactions such as filtering and sorting, and provide drill-down capabilities for deeper analysis. They are widely used in business intelligence, data analysis, and other domains where data-driven decision-making is important.

D3.js, the JavaScript library used to create D3 dashboards, is a popular tool for creating interactive and dynamic data visualizations on the web. It provides a wide range of powerful tools for creating and manipulating visual elements, such as scales, axes, shapes, and colors, and for handling data, such as filtering, grouping, and sorting.

D3 dashboards can be used to display data from a variety of sources, such as databases, CSV files, JSON files, and REST APIs. The data is usually processed and transformed into the format needed for visualization using JavaScript and D3.js tools. Once the data is prepared, the visualizations can be created and arranged in a dashboard format using HTML and CSS.

D3 dashboards can be highly customizable, allowing developers to create unique designs and styles to suit their specific needs. They can also be made responsive to different screen sizes and devices, ensuring that they are accessible to a wide range of users.

Overall, D3 dashboards are a powerful and flexible tool for visualizing data and providing insights in a dynamic and interactive way. They are widely used in business, finance, healthcare, and many other industries to help organizations make data-driven decisions.

Sure, here are a few D3 dashboard examples that you might find helpful:

Examples of D3 dashboard

Multi-series line chart: This dashboard example shows a multi-series line chart that displays data from different sources. It is useful for comparing data over time and identifying trends.
Heatmap: Heatmaps are useful for visualizing large data sets. This dashboard example shows a heatmap of stock prices over time.
Scatter plot: This dashboard example shows a scatter plot of housing prices versus square footage. It is useful for identifying trends and outliers.
Bar chart: Bar charts are useful for comparing data across categories. This dashboard example shows a bar chart of sales data by product category.
Map: Maps are useful for visualizing data geographically. This dashboard example shows a map of US states and the number of COVID-19 cases in each state.
Tree map: A tree map is useful for visualizing hierarchical data. This dashboard example shows a tree map of a company's organizational structure.
Bubble chart: Bubble charts are useful for visualizing data in three dimensions, with the size of each bubble representing one data point, the x-axis representing one variable, and the y-axis representing another variable. This dashboard example shows a bubble chart of a company's sales by product category, with the size of each bubble representing the total sales in each category.
Pie chart: Pie charts are useful for showing how a whole is divided into parts. This dashboard example shows a pie chart of a company's revenue by region, with each slice of the pie representing the revenue generated in a different region.
Combination chart: A combination chart combines multiple types of charts into one dashboard. This dashboard example shows a combination chart of a company's sales by product category, with a line chart showing the sales trends over time and a bar chart showing the sales by category.
Gauge chart: A gauge chart is useful for visualizing progress towards a goal or target. This dashboard example shows a gauge chart of a company's progress towards its revenue target for the year, with the gauge needle pointing to the current revenue as a percentage of the target revenue.

All of these examples use D3.js to create dynamic, interactive dashboards that allow users to explore and analyze data in a variety of ways. D3.js is a powerful tool for creating data visualizations and dashboards, and these examples demonstrate some of the many ways it can be used to create useful and informative visualizations.

Advantages and Disadvantages of D3 dashboard

D3 (Data-Driven Documents) is a popular JavaScript library for creating interactive data visualizations on the web. Here are some advantages and disadvantages of using D3 for building dashboards:

Advantages:

Highly customizable: D3 is highly customizable, allowing developers to create unique and highly specific data visualizations.
Interactive: D3 makes it easy to create interactive data visualizations, enabling users to interact with and explore the data in real-time.
Cross-platform compatibility: D3 can be used on various platforms, including desktop and mobile devices, as well as on different web browsers.
High performance: D3 is known for its high performance, especially when handling large datasets.
Free and open-source: D3 is free to use and is an open-source project, which means that users can access and modify the source code as needed.
Flexibility: D3 is a flexible library that allows developers to create any type of data visualization they need, from simple charts to complex interactive dashboards.
Integration: D3 can be integrated with other JavaScript libraries and frameworks, such as React and Angular, to create powerful and dynamic web applications.
Large user community: D3 has a large and active user community, which means that developers can access a wide range of tutorials, examples, and support resources.
Accessibility: D3 supports accessibility features, such as screen readers, which makes it possible to create dashboards that are accessible to people with disabilities.
Animations: D3 makes it easy to add animations to data visualizations, which can help to convey complex information in a more engaging and memorable way.
Interoperability: D3 provides a common platform for data visualization, which makes it possible to share and collaborate on visualizations across different platforms and tools.
Versatility: D3 supports a wide range of data formats, including CSV, JSON, and XML, which makes it possible to use data from a variety of sources in a dashboard.
Community-contributed code: D3 has a large and active user community, which has contributed a significant amount of code, examples, and plugins that can help developers build dashboards more quickly and easily.
Branding and styling: D3 makes it easy to brand and style data visualizations, which can help to create a consistent and professional look and feel for a dashboard.
Scalability: D3 is scalable, which means that it can be used to create small or large dashboards, depending on the needs of the project.

Disadvantages:

Steep learning curve: D3 has a steep learning curve, as it requires developers to have a strong understanding of JavaScript, HTML, and CSS.
Time-consuming: Building a dashboard with D3 can be time-consuming, as it requires a lot of coding and customization to create a tailored solution.
Browser compatibility issues: D3 is known to have browser compatibility issues, and developers may need to add additional code to ensure the dashboard works properly across different browsers.
Lack of documentation: While D3 has a large and active user community, it can be challenging to find detailed documentation on some aspects of the library, which can make it more difficult for developers to troubleshoot issues.
Maintenance: Dashboards built with D3 require ongoing maintenance and updates, especially as new versions of the library are released.
Performance trade-offs: While D3 is known for its high performance, complex and interactive visualizations can still lead to performance issues, particularly on older devices or slower internet connections.
Lack of interactivity on some devices: Some older web browsers or mobile devices may not support the interactive features of D3, which can limit the functionality of the dashboard.
Compatibility with legacy systems: D3 may not be compatible with older systems or legacy software, which can limit its use in certain contexts.
Security: D3 is a client-side library, which means that it can be vulnerable to security issues such as cross-site scripting (XSS) attacks if not implemented properly. Developers need to take extra care to ensure the security of their D3 dashboards.
Complexity: While D3 is flexible and customizable, it can also be complex to work with, especially for developers who are new to the library. This can make it more difficult to create a dashboard quickly and efficiently.
Limited out-of-the-box functionality: D3 is primarily a library for creating custom data visualizations, which means that it does not have a lot of pre-built dashboard components or templates. This can make it more difficult to build a dashboard from scratch.
Accessibility limitations: While D3 does support accessibility features, creating truly accessible dashboards with D3 can be challenging, especially for developers who are not familiar with accessibility guidelines and best practices.
Version dependency: D3 is constantly evolving, which means that code written for one version of the library may not work with newer versions. This can create challenges when it comes to maintaining and updating a dashboard over time.
Time and resource constraints: Building a dashboard with D3 can be time-consuming, especially if the project requires a high level of customization or interactivity. This can be challenging for developers who are working with limited time and resources.
Learning curve for end-users: If end-users are not familiar with D3 or data visualization concepts, they may find it difficult to understand and interpret the information presented in a dashboard. This can create usability and adoption challenges.

Miscellaneous

Miscellaneous

D3 Dashboard with Examples

Examples of D3 dashboard

Advantages and Disadvantages of D3 dashboard