Metadata deployment
August 8, 2023
Metadata-driven vs. configuration data-driven apps FAQ
What's the difference?
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For any Salesforce professional, it’s essential to understand the differences between metadata-driven and configuration-data driven applications. After all, the architecture behind an app shapes its performance—plus, it plays an important role in the change management process.
Both types of architecture bring unique strengths to the table in application lifecycle management (ALM). Metadata provides a clear, organized framework for making changes. On the other hand, configuration data is more customizable and requires a more nuanced approach. And as we’ll see, automation is a must-have for configuration data change management because it ensures consistent, error-free application behavior.
The following FAQs get into the nitty-gritty of metadata-driven vs. configuration data-driven apps.
The Salesforce platform stores customizations and configurations as metadata that determines the behavior of an app. For example, on the Lightning Platform, developers don't have to code every single behavior. Instead, they define the metadata, and the platform interprets their definitions to trigger the specified functionality or behavior.
Unlike rigid lines of code, metadata is flexible and adaptable to your needs. You don’t have to go into the backend and plough through endless lines of code whenever you want to make a minor change. It’s a game-changer for business agility because you can respond faster to change requests.
With a configuration data-driven architecture, the app's behavior and logic are driven by configurable data that’s stored in regular database tables. This makes the application highly customizable. Think of it like a dashboard for a car where you can adjust settings according to the needs of the route you’re driving. You can control those settings to tweak and tailor the applications functions on the fly.
Metadata has more abstract and generalized rules, while configuration data lets you make granular changes—like a set of buttons and dials to fine-tune your Salesforce application.
Examples of apps built on configuration data are Salesforce CPQ, Advanced Approvals, Kaptio, Conga Composer, Conga CLM, and B2B Commerce.
At its core, configuration data is about user empowerment and flexibility. You don’t need in-depth programmatic knowledge to alter the app’s behavior through simple configuration changes.
Sometimes business users need to pivot on a dime—and that involves making changes to Salesforce. In a traditional setup, you’d call in a developer, discuss the change request, and wait for them to build, test, and deploy those changes. The potential for miscommunication in this often drawn-out process is significant.
Configuration data turns this process on its head. For instance, if you adjust a sales process, you can easily alter it in a configuration data-driven app like Salesforce CPQ. With a few simple clicks, you can add rules or modify stages to reflect the new sales process.
As a result, admins and other declarative developers can make changes without needing in-depth understanding of code. This speeds up response times, eliminates bottlenecks, and streamlines the release process so your business keeps humming along efficiently.
It’s impossible to accurately compare the total cost of ownership (TCO) for the two types of apps because each use case is unique. That said, there are some broad generalizations.
Metadata-driven solutions can come with a higher sticker price—primarily due to the length and complexity of initial setup, which can really slow things down. Making changes after implementation can also be a lengthy process that requires a considerable investment of resources.
In contrast, configuration data-driven apps are often easier to set up and have a shorter go-live time, which often delivers a quicker ROI. On top of that, they offer ease of use and more flexibility. Over time, this can translate into sizable time savings—and as a result, cost savings.
In terms of data protection, the beauty of a metadata-based architecture lies in its integrated nature. Developers commonly embed standardized security settings directly into the metadata itself. This provides a consistent level of broad, application-wide security. Think of it like a fortress with strong outer walls—once safely inside, everything within those walls benefits from the same protection.
Configuration data-driven architecture offers a scalpel-like precision that lets you tailor security protocols to your specific needs. Imagine installing individual security systems for the lobby, gift shop, and exhibit halls in a museum. You need to secure the lobby to prevent unwanted intruders. The gift shop requires a higher level of security than the lobby because it’s where financial transactions take place. Finally, the exhibit halls require the highest level of security because the artifacts or art are the most valuable items in the building.
Configuration data’s approach to security might require more initial setup, but it offers the advantage that you can respond dynamically to any emerging threats.
The objective of Salesforce ALM is to introduce changes to your production org smoothly and effectively. However, the complexity of a configuration data-based architecture can make this challenging. Configuration data is often related to a host of other data—and you need to take all those relationships into account in the change management process.
Let’s say you want to make a change to a data point that’s related to multiple objects. One seemingly insignificant tweak could inadvertently change how these objects interact—or even disrupt a critical workflow. Think of it like adjusting a gear in a clock. You can’t change one without considering the others or you might throw off the time.
In short, with configuration data, you have to meticulously track and review every change to ensure all connections remain intact. This is where an automation tool like Prodly comes in—it seamlessly manages all relationships so you don’t have to. It saves a ton of time, prevents errors, and eliminates bottlenecks in the pipeline.
A metadata-driven architecture provides a consistent interface, which results in a certain degree of uniformity across the board. Plus, everyone from new hires to veteran team members experiences a sense of familiarity after updates.
While configuration data-driven apps are ultra flexible, they require a lot of manual work unless you use automation. Automation like Prodly’s templates for Salesforce CPQ, B2B Commerce, Advanced Approvals, or more is super simple to use.
Here's how easy it is to run a CPQ deployment, for example.
Metadata’s intrinsic stability supports scalability. Since the core structure of metadata-driven apps doesn't change drastically, scaling up is relatively smooth and straightforward.
Configuration data lends itself to customization, so scaling these apps is relatively painless. Whether you’re launching a new product line or adding a new territory, you can quickly and easily configure the app to meet your needs. In addition, these apps integrate seamlessly with other applications, which helps ensure a smooth workflow.
Version control (or source control) for configuration data offers several benefits. It actively records every single change so you can always review the what, when, why, and by whom of every change. If you use an app like Salesforce CPQ, this helps you meet the documentation requirement of SOX and other financial regulations.
A version control system (VCS) also promotes collaboration because it minimizes the chances of team members overwriting each other’s work or having problems integrating their builds. If anything goes wrong after deployment, you can simply roll back the changes. Plus, conflict detection and resolution become a piece of cake, because you can review all changes and pinpoint where the issue lies.
These frequently asked questions provide an overview of the difference between metadata-driven and configuration data-driven architectures. While each has its own unique attributes, they both play a fundamental role in the ALM process. By understanding both types of architecture, you can more easily navigate the complexities of Salesforce ALM.
For any Salesforce professional, it’s essential to understand the differences between metadata-driven and configuration-data driven applications. After all, the architecture behind an app shapes its performance—plus, it plays an important role in the change management process.
Both types of architecture bring unique strengths to the table in application lifecycle management (ALM). Metadata provides a clear, organized framework for making changes. On the other hand, configuration data is more customizable and requires a more nuanced approach. And as we’ll see, automation is a must-have for configuration data change management because it ensures consistent, error-free application behavior.
The following FAQs get into the nitty-gritty of metadata-driven vs. configuration data-driven apps.
The Salesforce platform stores customizations and configurations as metadata that determines the behavior of an app. For example, on the Lightning Platform, developers don't have to code every single behavior. Instead, they define the metadata, and the platform interprets their definitions to trigger the specified functionality or behavior.
Unlike rigid lines of code, metadata is flexible and adaptable to your needs. You don’t have to go into the backend and plough through endless lines of code whenever you want to make a minor change. It’s a game-changer for business agility because you can respond faster to change requests.
With a configuration data-driven architecture, the app's behavior and logic are driven by configurable data that’s stored in regular database tables. This makes the application highly customizable. Think of it like a dashboard for a car where you can adjust settings according to the needs of the route you’re driving. You can control those settings to tweak and tailor the applications functions on the fly.
Metadata has more abstract and generalized rules, while configuration data lets you make granular changes—like a set of buttons and dials to fine-tune your Salesforce application.
Examples of apps built on configuration data are Salesforce CPQ, Advanced Approvals, Kaptio, Conga Composer, Conga CLM, and B2B Commerce.
At its core, configuration data is about user empowerment and flexibility. You don’t need in-depth programmatic knowledge to alter the app’s behavior through simple configuration changes.
Sometimes business users need to pivot on a dime—and that involves making changes to Salesforce. In a traditional setup, you’d call in a developer, discuss the change request, and wait for them to build, test, and deploy those changes. The potential for miscommunication in this often drawn-out process is significant.
Configuration data turns this process on its head. For instance, if you adjust a sales process, you can easily alter it in a configuration data-driven app like Salesforce CPQ. With a few simple clicks, you can add rules or modify stages to reflect the new sales process.
As a result, admins and other declarative developers can make changes without needing in-depth understanding of code. This speeds up response times, eliminates bottlenecks, and streamlines the release process so your business keeps humming along efficiently.
It’s impossible to accurately compare the total cost of ownership (TCO) for the two types of apps because each use case is unique. That said, there are some broad generalizations.
Metadata-driven solutions can come with a higher sticker price—primarily due to the length and complexity of initial setup, which can really slow things down. Making changes after implementation can also be a lengthy process that requires a considerable investment of resources.
In contrast, configuration data-driven apps are often easier to set up and have a shorter go-live time, which often delivers a quicker ROI. On top of that, they offer ease of use and more flexibility. Over time, this can translate into sizable time savings—and as a result, cost savings.
In terms of data protection, the beauty of a metadata-based architecture lies in its integrated nature. Developers commonly embed standardized security settings directly into the metadata itself. This provides a consistent level of broad, application-wide security. Think of it like a fortress with strong outer walls—once safely inside, everything within those walls benefits from the same protection.
Configuration data-driven architecture offers a scalpel-like precision that lets you tailor security protocols to your specific needs. Imagine installing individual security systems for the lobby, gift shop, and exhibit halls in a museum. You need to secure the lobby to prevent unwanted intruders. The gift shop requires a higher level of security than the lobby because it’s where financial transactions take place. Finally, the exhibit halls require the highest level of security because the artifacts or art are the most valuable items in the building.
Configuration data’s approach to security might require more initial setup, but it offers the advantage that you can respond dynamically to any emerging threats.
The objective of Salesforce ALM is to introduce changes to your production org smoothly and effectively. However, the complexity of a configuration data-based architecture can make this challenging. Configuration data is often related to a host of other data—and you need to take all those relationships into account in the change management process.
Let’s say you want to make a change to a data point that’s related to multiple objects. One seemingly insignificant tweak could inadvertently change how these objects interact—or even disrupt a critical workflow. Think of it like adjusting a gear in a clock. You can’t change one without considering the others or you might throw off the time.
In short, with configuration data, you have to meticulously track and review every change to ensure all connections remain intact. This is where an automation tool like Prodly comes in—it seamlessly manages all relationships so you don’t have to. It saves a ton of time, prevents errors, and eliminates bottlenecks in the pipeline.
A metadata-driven architecture provides a consistent interface, which results in a certain degree of uniformity across the board. Plus, everyone from new hires to veteran team members experiences a sense of familiarity after updates.
While configuration data-driven apps are ultra flexible, they require a lot of manual work unless you use automation. Automation like Prodly’s templates for Salesforce CPQ, B2B Commerce, Advanced Approvals, or more is super simple to use.
Here's how easy it is to run a CPQ deployment, for example.
Metadata’s intrinsic stability supports scalability. Since the core structure of metadata-driven apps doesn't change drastically, scaling up is relatively smooth and straightforward.
Configuration data lends itself to customization, so scaling these apps is relatively painless. Whether you’re launching a new product line or adding a new territory, you can quickly and easily configure the app to meet your needs. In addition, these apps integrate seamlessly with other applications, which helps ensure a smooth workflow.
Version control (or source control) for configuration data offers several benefits. It actively records every single change so you can always review the what, when, why, and by whom of every change. If you use an app like Salesforce CPQ, this helps you meet the documentation requirement of SOX and other financial regulations.
A version control system (VCS) also promotes collaboration because it minimizes the chances of team members overwriting each other’s work or having problems integrating their builds. If anything goes wrong after deployment, you can simply roll back the changes. Plus, conflict detection and resolution become a piece of cake, because you can review all changes and pinpoint where the issue lies.
These frequently asked questions provide an overview of the difference between metadata-driven and configuration data-driven architectures. While each has its own unique attributes, they both play a fundamental role in the ALM process. By understanding both types of architecture, you can more easily navigate the complexities of Salesforce ALM.
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