• As the number of columns increases, the recommended record capacity decreases  

• Not suitable for storing large-scale or high-frequency transactional data  

Performance Considerations

Frequent operations can impact overall system performance: 

• High-frequency imports  

• Frequent updates or reads  

• Repeated refresh operations  

Potential impacts: 

• Increased hardware utilization  

• Slower API response times  

• Delays in processing  

Fair Usage Guidelines 

Table Design Best Practices

• Keep the number of tables limited and purposeful  

• Use only the necessary columns  

• Avoid over-complicating the schema  

Data Volume Management

Maintain controlled limits on: 

• Number of tables  

• Number of columns (recommended: 10–15)  

• Number of records  

Best practices:

• Archive old data  

• Perform periodic cleanup 

Import & Refresh Strategy 

• Avoid frequent imports (e.g., multiple times per hour)  

• Prefer batch processing over real-time updates  

• Limit unnecessary refresh or re-index operations  

 Supported Data Types and Field Limits 

• Custom Tables support the following data types: 

• Text – up to 4000 characters  

• Text Area – up to 8000 characters  

• Number – supports up to 38 digits (precision/scale 38/38)  

• Boolean  

• Date and Time  

Guideline: Select appropriate data types to optimize storage and performance. 

Usage Guidelines: When to Use vs Avoid 

Use Custom Tables When:

• Data is small, stable, and structured  

• Use case is configuration, reference, or lightweight extension  

• No complex relationships are required  

• Data is not available in standard platform features  

Avoid Using Custom Tables When:

• Data is large or frequently changing  

• Relationships between datasets are required  

• Use case involves real-time or transactional systems  

• Data is expected to grow significantly over time  

Best Practices 

• Keep table structure simple and minimal  

• Limit columns and records  

• Avoid frequent updates or imports  

• Regularly clean up unused data 

Recommended Use Cases (B2B)

Custom Tables are most effective when used for small, controlled, and well-defined extensions such as: 

Temporary or Transitional Data 

• Storing short-term data during:  
  • Data migration
  • Feature rollout
  • Interim processes  

• Examples:
  • Maintaining mappings between legacy system identifiers and new system identifiers during migration 

External System Reference Mapping 

• Mapping identifiers between external systems and the platform  

• Maintaining a small, stable integration reference data  

• Examples: 

• Mapping ERP Customer ID to Znode Customer ID  

• Linking External Product ID with Znode Product SKU  

• Storing reference IDs used for syncing data with third-party systems 

UI-Level Data Extensions 

• Used for storing additional information intended for UI display purposes, such as: 

• Enhancing existing screens with supplementary fields  

• Displaying non-critical, contextual information to users 

• Examples: 

• Adding a “Special Handling Note” field on detail pages  

• Displaying a “Priority” or “Status” label within UI screens  

• Showing an additional field like “Internal Remarks” in detail views 

Simple Data Mapping 

• Mapping small datasets such as:  

• Customer to Tag  

• Product to Label  

• Region to Attribute  

• Example: 

• Assigning a tag like “Premium” or “New” to specific customers  

• Adding a label like “Seasonal” or “Clearance” to certain products  

• Mapping regions to a category like “Urban” or “Rural.” 

Basic Business Rules 

• Managing simple conditions or flags  

• Supporting threshold-based or eligibility-based logic  

• Examples: 

• Marking orders as “High Value Order” if the amount is above a certain limit  

• Flagging customers as “Eligible for Free Shipping” based on order value  

• Setting a simple rule like “Allow Bulk Orders” → Yes / No for selected customers 

Configuration & Lookup Data 

• Storing business-specific codes, labels, or categories  

• Maintaining small lookup datasets  

• Examples: 

• Mapping internal customer types like CUST_TYPE_1 to Retail Customer or Wholesale Customer  

• Creating a list of order statuses like Pending, Approved, On Hold  

• Defining categories such as Gold, Silver, Bronze for customer segmentation 

 Reporting Support Data (Lightweight) 

• Storing small datasets used for filtering or categorization in reports  

• Examples: 
  • Tagging products as “High Margin” or “Low Margin” for analysis  
  • Creating custom report filters like “Priority Customers” 

 Feature Flags & Conditional Controls 

• Enabling or disabling features for specific customers or scenarios  

• Managing simple yes/no or condition-based settings  

• Examples: 
  • Marking customers as “Eligible for Express Checkout” → Yes / No  
  • Enabling a “Special Discount” feature only for selected customers  

Data Maintenance & Growth Considerations 

Data Growth  

Always consider how your data will grow over time. 

If your data is expected to: