Use Cases & Examples

Use Cases & Examples

Explore real-world implementations of Spartera across various industries and
use cases.

Sports Analytics Example

Let's explore how our API can be used to analyze sports data and create
valuable assets.

Scenario: NBA Player Performance Analytics

Python

from spartera_api_sdk import ApiClient, Configuration
from spartera_api_sdk.api import assets_api

## Configure client
config = Configuration()
config.host = "https://api.spartera.com"
config.api_key = {'X-API-Key': 'your-api-key-here'}
client = ApiClient(config)

## Create a Single-Column Calculation Asset
asset_data_calc = {
    "accept_terms": True,
    "asset_type": "CALCULATION",
    "company_id": "your-company-id",
    "connection_id": "nba-stats-connection",
    "description": "Calculate average NBA player efficiency rating",
    "name": "Average NBA Player Efficiency Rating",
    "sql_logic": "SELECT AVG((points + rebounds + assists + steals + blocks - (field_goals_attempted - field_goals_made) - (free_throws_attempted - free_throws_made) - turnovers) / games_played) FROM nba_player_stats WHERE season = '2024-25' AND games_played >= 20",
    "user_id": "your-user-id"
}

## Create a Two-Column Visualization Asset
asset_data_vis = {
    "accept_terms": True,
    "asset_type": "VISUALIZATION",
    "company_id": "your-company-id",
    "connection_id": "nba-stats-connection",
    "description": "Top NBA player efficiency ratings visualization",
    "name": "Top NBA Player Efficiency Ratings",
    "viz_chart_type": "BAR",
    "schema_table": "nba_stats.player_stats",
    "viz_dep_var_col_name": "efficiency_rating",
    "viz_indep_var_col_name": "player_name",
    "viz_data_limit": 10,
    "viz_color_scheme": "Default",
    "viz_data_aggregation": "No Aggregation",
    "viz_sort_direction": "Descending",
    "visibility": "PUBLIC",
    "source": "MANUAL",
    "active": True,
    "user_id": "your-user-id",
    "viz_chart_library": "PLOTLY"
}

Industry Use Cases

1. E-commerce Analytics

Customer Lifetime Value Prediction

SQL

SELECT AVG(estimated_clv)
FROM (
    SELECT 
        AVG(order_value) * COUNT(DISTINCT order_id) * 
        (DATEDIFF(CURRENT_DATE, first_purchase_date) / 365.0) 
        as estimated_clv
    FROM customer_orders 
    GROUP BY customer_id
) AS subquery

2. Financial Services

Credit Risk Assessment

SQL

SELECT COUNT(*)
FROM loan_applications
WHERE credit_score < 650 OR debt_to_income >= 0.5

3. Healthcare Analytics

Patient Readmission Risk

SQL

SELECT 
    SUM(CASE WHEN age > 65 AND comorbidity_count > 3 THEN 1 ELSE 0 END) * 
    100.0 / COUNT(*) AS high_risk_percentage
FROM patient_admissions

4. Marketing Intelligence

Campaign Performance Optimization

Single-Column Calculation: Get the average Return on Ad Spend (ROAS).

SQL

SELECT AVG(return_on_ad_spend)
FROM marketing_campaigns
WHERE campaign_start_date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)

Two-Column Visualization (Line Chart): Show the conversion rate by marketing
channel.

SQL

SELECT 
    channel,
    AVG(conversion_rate) AS average_conversion_rate
FROM marketing_campaigns
GROUP BY channel
ORDER BY average_conversion_rate DESC

5. Supply Chain Optimization

Demand Forecasting

SQL

SELECT AVG(sales_quantity)
FROM sales_history
WHERE product_id = 'product_123'

Integration Examples

Dashboard Integration (Power BI)

Python

import requests
import pandas as pd

def get_spartera_insight(asset_id, params=None):
    headers = {'X-API-Key': 'your-api-key'}
    url = f"https://api.spartera.com/insights/{asset_id}/execute"
    
    response = requests.post(url, headers=headers, json=params)
    return pd.DataFrame(response.json()['data'])

## Use in Power BI Python script
df = get_spartera_insight('nba-player-efficiency')

Mobile App Integration

JavaScript

// React Native example
import { SparteraClient } from 'spartera-js-sdk';

const client = new SparteraClient({
  apiKey: 'your-api-key',
  baseURL: 'https://api.spartera.com'
});

async function loadPlayerStats() {
  const result = await client.insights.execute({
    assetId: 'nba-player-efficiency',
    parameters: {
      team: 'Lakers',
      season: '2024-25'
    }
  });
  
  return result.data;
}

Real-time Streaming

Python

import asyncio
from spartera_api_sdk.websockets import WebSocketClient

async def stream_live_scores():
    client = WebSocketClient('wss://api.spartera.com/stream')
    
    await client.subscribe('sports-scores-live', {
        'leagues': ['NBA', 'NFL'],
        'teams': ['Lakers', 'Patriots']
    })
    
    async for message in client.listen():
        print(f"Live update: {message}")

## Run the streaming client
asyncio.run(stream_live_scores())