Business Intelligence (BI) tools are essential for analyzing data and making informed decisions. The approach to BI can significantly vary, especially when comparing BI tooling based on "BI as code" to drag-and-drop alternatives. The latter has been around for some time now, lets see if the tools with a new approach are going to fit in the market. How might they change the landscape?
Here are the key differences between these two approaches:
1. Development Approach
- BI as Code: This approach treats BI development similarly to software development, where you define data models, transformations, and visualizations as code. It often involves using version control systems, which allows for better tracking of changes, collaboration, maintainability and deployment processes. Tools like dbt (data build tool) and evidence.dev exemplify this approach.
- Drag and Drop: These tools are designed for ease of use, allowing users to create reports, dashboards, and perform data analysis through a graphical user interface (GUI) without writing any code. Tools like Tableau, Qlik Sense and Power BI are prominent examples. They are particularly appealing to users who may not have a strong background in programming.
2. User Skill Set
- BI as Code: Requires users to have a good understanding of programming concepts and be comfortable working with code. This approach is often favored by data engineers and developers.
- Drag and Drop: Designed for a broader audience, including business analysts, marketers, and other non-technical users. It lowers the barrier to entry for performing data analysis.
3. Collaboration and Version Control
- BI as Code: Facilitates collaboration through version control systems like Git. Changes to BI projects are tracked, reviewed, and merged systematically, similar to how software development projects are managed.
- Drag and Drop: While these tools often have features for sharing and collaborating on reports or dashboards, they typically lack the robust version control and code review processes found in a "BI as code" approach.
4. Customization and Flexibility
- BI as Code: Offers high levels of customization and flexibility. Since everything is code-based, you can tailor every aspect of the BI process to fit specific requirements. This is particularly useful for complex data transformations and custom analytics.
- Drag and Drop: While user-friendly and quick to set up, these tools may have limitations when it comes to customization. You are generally constrained by the functionalities provided by the GUI.
5. Deployment and Scalability
- BI as Code: The code-based nature allows for seamless integration with CI/CD pipelines, making it easier to deploy and scale BI solutions across different environments and platforms.
- Drag and Drop: Deployment and scalability can be more manual and less flexible, often depending on the capabilities of the tool's vendor. Some tools, however, are offering more advanced deployment options to address these challenges.
Conclusion
Choosing between "BI as code" and drag-and-drop tools depends on your team's skills, the complexity of your data, and your need for collaboration and customization. Both have their strengths and are suited to different situations.
We'll be diving deeper into BI as code, especially focusing on tools like Evidence.dev, in future articles on Fact.ist. Stay tuned!
FAQs
1. What is "BI as code"?
"BI as code" refers to the approach of treating Business Intelligence (BI) development similarly to software development. It involves defining data models, transformations, and visualizations using code, often leveraging version control systems for better tracking, collaboration, maintainability, and deployment processes.
2. How does "BI as code" differ from drag-and-drop BI tools?
The key differences are:
- Development Approach: "BI as code" uses coding for BI tasks, while drag-and-drop tools use a graphical user interface (GUI).
- User Skill Set: "BI as code" requires programming knowledge, whereas drag-and-drop tools are designed for non-technical users.
- Collaboration and Version Control: "BI as code" facilitates collaboration through version control systems like Git, while drag-and-drop tools may lack robust version control features.
- Customization and Flexibility: "BI as code" offers high customization and flexibility, whereas drag-and-drop tools are more limited.
- Deployment and Scalability: "BI as code" integrates well with CI/CD pipelines, making deployment and scalability easier. Drag-and-drop tools may have more manual and less flexible deployment options.
3. Who benefits most from using "BI as code"?
"BI as code" is particularly beneficial for data engineers, developers, and teams with a strong programming background. It is ideal for complex data transformations, custom analytics, and environments where collaboration and version control are critical.
4. What are some examples of "BI as code" and drag-and-drop BI tools?
Examples of "BI as code" tools include dbt (data build tool) and Evidence.dev. Examples of drag-and-drop BI tools are Tableau, Qlik Sense, and Power BI.
5. How do I choose between "BI as code" and drag-and-drop tools for my team?
The choice depends on:
- Team Skills: If your team has strong programming skills, "BI as code" might be more suitable. For teams with less technical expertise, drag-and-drop tools are easier to use.
- Complexity of Data: For complex data transformations and custom analytics, "BI as code" offers more flexibility. Drag-and-drop tools are better for straightforward data analysis.
- Collaboration Needs: If robust version control and collaboration are important, "BI as code" is advantageous. Drag-and-drop tools may suffice for simpler collaboration needs.
- Customization Requirements: "BI as code" provides high levels of customization, making it suitable for tailored BI processes. Drag-and-drop tools are limited to the functionalities provided by their GUI.