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How can web application frameworks be used to create business intelligence (BI) functionality?

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Web application frameworks are tools that help developers create and maintain web applications, such as websites, web services, or web APIs. They provide common features, such as routing, templating, security, and database access, that can save time and effort. But can they also be used to create business intelligence (BI) functionality? BI is the process of analyzing data and presenting insights that can help businesses make informed decisions. In this article, we will explore how web application frameworks can support BI in four ways: data collection, data processing, data visualization, and data interaction.

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1 Data collection

One of the first steps in BI is to collect data from various sources, such as user behavior, transactions, feedback, or external APIs. Web application frameworks can help with this by providing built-in or third-party modules that can handle data ingestion, validation, and storage. For example, Django, a popular Python web framework, has a powerful ORM (object-relational mapper) that can work with different databases and perform CRUD (create, read, update, delete) operations. It also has a REST framework that can create and consume web APIs. These features can make data collection easier and more consistent.

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Lucian Ursu

Senior Software Engineer | Contractor
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Frankly, it might make more sense for a business to use dedicated tool like Power BI (there might be open-source ones as well, I don't know, I don't work in this space). Coding one from scratch sounds very inefficient.

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2 Data processing

Once the data is collected, it needs to be processed to extract meaningful information and prepare it for analysis. Web application frameworks can help with this by providing tools that can perform data manipulation, transformation, and aggregation. For example, Flask, a lightweight Python web framework, has a Pandas extension that can integrate the Pandas library, which is a powerful tool for data analysis and manipulation. It can also use Celery, a distributed task queue, to perform asynchronous and parallel data processing tasks. These tools can help speed up and scale up data processing.

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3 Data visualization

Another important step in BI is to present the data in a visual and interactive way that can reveal patterns, trends, and insights. Web application frameworks can help with this by providing libraries that can generate charts, graphs, dashboards, and reports. For example, Ruby on Rails, a popular Ruby web framework, has a Chartkick gem that can create beautiful charts with a simple API. It also has a StimulusReflex library that can enable real-time updates and interactions with the data. These libraries can help create engaging and dynamic data visualizations.

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4 Data interaction

The final step in BI is to allow users to interact with the data and explore different scenarios and options. Web application frameworks can help with this by providing components that can create forms, filters, buttons, sliders, and other widgets that can modify the data or the visualization. For example, Laravel, a popular PHP web framework, has a Livewire package that can create reactive and interactive web interfaces with minimal JavaScript. It also has a Nova tool that can create custom admin panels and CRUD interfaces for the data. These components can help create user-friendly and flexible data interaction.

Web application frameworks can be used to create BI functionality by providing features and tools that can handle data collection, data processing, data visualization, and data interaction. They can help developers create and maintain web applications that can deliver valuable insights and solutions for businesses.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can web application frameworks be used to create business intelligence (BI) functionality?

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1 Data collection

2 Data processing

3 Data visualization

4 Data interaction

5 Here’s what else to consider

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