API Reference

Backend Platform Best Practices

Backend Platform Best Practices

To get the best results from Spartera's AI-powered analytics platform
and create high-quality assets, follow these essential backend data
architecture best practices. These guidelines ensure optimal
performance, accurate insights, and seamless AI comprehension.

Core Principles

Effective backend architecture for AI analytics relies on four
fundamental principles:

  • Simplicity: Structure data in ways that AI models can easily
    understand and process
  • Clarity: Use descriptive naming and clear business context in
    your data models
  • Performance: Optimize for fast query execution and insight
    generation
  • Quality: Maintain clean, consistent, and reliable data structures

Essential Best Practices

Denormalize Your Tables

A single, wide table is easier for AI to query and understand than
complex normalized schemas.

  • Flatten related data into comprehensive business-focused tables
  • Reduce complex JOINs that can confuse AI interpretation
  • Maintain referential integrity while optimizing for analytics

Key Benefits

  • Faster query performance
  • Better AI pattern recognition
  • Simplified data relationships

Use Meaningful Column Names

Descriptive column names like total_points or game_date help AI
models understand context better than generic names like c1 or
col_2.

  • Choose business-friendly terminology over technical abbreviations
  • Include units and context in column names when relevant
  • Maintain consistent naming conventions across all tables

Examples

  • customer_lifetime_value_usd instead of clv
  • order_date instead of dt1
  • monthly_recurring_revenue instead of mrr

Clean Your Data

Handle missing values, remove inconsistencies, and ensure data quality
before analysis.

  • Implement data validation rules at ingestion
  • Remove or flag test and placeholder data
  • Standardize formats and handle null values consistently
  • Document data quality metrics and thresholds

Use Correct Data Types

Ensure each column uses appropriate data types for optimal performance
and accuracy.

  • Numbers as DECIMAL or INTEGER, not strings
  • Dates as DATE or TIMESTAMP with proper formatting
  • Text as VARCHAR with appropriate length limits
  • Boolean values as BOOLEAN, not 0/1 integers

Architecture Guidelines

1. Denormalized Tables

Learn how to effectively flatten your data structures for AI analytics:

  • Star schema flattening techniques
  • Event aggregation strategies
  • Performance optimization methods
  • Common denormalization patterns

2. Proper Record Granularity

Choose the right level of detail for your analytics requirements:

  • Time-based granularity selection
  • Entity-level aggregation strategies
  • Performance vs detail trade-offs
  • Multi-layer granularity approaches

3. Raw vs Semantic Layers

Implement a clean separation between raw data storage and business-ready
analytics:

  • Raw layer design principles
  • Semantic layer business logic
  • Data quality and governance
  • Performance optimization patterns

4. Chart Type Selection

Select the optimal visualization for your data and analytical purpose:

  • Purpose-driven chart selection
  • Spartera-supported chart types
  • Data structure optimization
  • Performance considerations

Implementation Checklist

Before deploying your backend architecture on Spartera:

  • Tables are denormalized for AI consumption
  • Column names are descriptive and business-friendly
  • Data quality rules are implemented and validated
  • Correct data types are used throughout
  • Raw and semantic layers are properly separated
  • Record granularity matches analytical requirements
  • Chart data is structured for optimal visualization
  • Performance benchmarks meet requirements

Data Quality Standards

For AI-Generated Insights

  • Use descriptive column names that provide business context
  • Include comprehensive data documentation and comments
  • Ensure consistent data formatting and structure across tables
  • Remove or clearly identify test and placeholder data
  • Implement data validation and quality monitoring

Documentation Requirements

  • Business definitions for all calculated fields
  • Data lineage and source system mapping
  • Update frequency and refresh schedules
  • Data quality thresholds and monitoring alerts
  • Business rules and transformation logic

Performance Optimization

Query Performance

  • Create appropriate indexes for common access patterns
  • Use partitioning for large time-series datasets
  • Implement materialized views for complex calculations
  • Monitor and optimize slow-running queries

Storage Efficiency

  • Apply compression for historical data
  • Implement data archiving strategies
  • Use appropriate data types to minimize storage
  • Balance denormalization benefits with storage costs

Getting Started

  1. Assess Current Architecture: Review existing data structures
    against these best practices
  2. Prioritize Improvements: Focus on high-impact changes first
  3. Implement Incrementally: Make changes in phases to minimize
    disruption
  4. Monitor and Validate: Track performance and quality metrics
  5. Iterate and Improve: Continuously refine based on usage patterns

Next Steps

For detailed implementation guidance, refer to the specific best
practice documents:

  • Denormalized Tables: Comprehensive denormalization strategies
  • Record Granularity: Choosing optimal data detail levels
  • Raw vs Semantic Layers: Data architecture patterns
  • Chart Type Selection: Visualization optimization guide

Following these backend best practices will ensure your Spartera
analytics assets deliver maximum value with optimal performance and
accuracy.