1. What is a Data Product?

A data product is a dataset that is treated as a product — with clear ownership, SLAs, documentation, and consumers.

Data Product Components:
├── Schema & Documentation
├── SLAs (freshness, completeness, availability)
├── Quality Checks (automated validation)
├── Access Controls (who can use it)
├── Lineage (where it comes from)
├── Versioning (breaking changes)
└── Monitoring (health dashboards)

2. Data Product Schema

from dataclasses import dataclass, field
from typing import Dict, List

@dataclass
class DataProduct:
    name: str
    domain: str
    description: str
    owner: str
    team: str
    schema: Dict[str, str]
    sla: Dict[str, any]
    quality_checks: List[str]
    access_pattern: str  # "batch", "streaming", "api"
    version: str = "1.0.0"
    status: str = "active"
    consumers: List[str] = field(default_factory=list)
    tags: List[str] = field(default_factory=list)
    
    def to_contract(self):
        return {
            "product": self.name,
            "version": self.version,
            "schema": self.schema,
            "sla": self.sla,
            "quality": self.quality_checks,
            "owner": f"{self.team}/{self.owner}",
            "status": self.status,
        }

# Example
orders_product = DataProduct(
    name="orders_fact",
    domain="commerce",
    description="All customer orders with line items",
    owner="alice",
    team="checkout-team",
    schema={"order_id": "string", "user_id": "string", "amount": "decimal", "status": "string"},
    sla={"freshness_minutes": 15, "availability": 99.95, "completeness": 99.9},
    quality_checks=["not_null(order_id)", "unique(order_id)", "positive(amount)"],
    access_pattern="batch",
    tags=["finance", "core", "pii"],
)

3. Product Metrics

class ProductMetrics:
    def __init__(self, product: DataProduct):
        self.product = product
    
    def compute_health_score(self, metrics: dict) -> float:
        weights = {"freshness": 25, "completeness": 25, "usage": 20, "quality": 30}
        score = 0
        
        if metrics.get("freshness_ok"): score += weights["freshness"]
        if metrics.get("completeness", 0) >= 0.99: score += weights["completeness"]
        if metrics.get("daily_queries", 0) > 0: score += weights["usage"]
        if metrics.get("quality_score", 0) >= 0.95: score += weights["quality"]
        
        return score
    
    def adoption_rate(self, total_users: int) -> float:
        return len(self.product.consumers) / total_users if total_users > 0 else 0

Follow-Up Questions

1. How would you measure the success of a data product?
2. Design a data product marketplace for internal teams.
3. How do you handle versioning and backward compatibility?
4. Design a self-serve portal for creating new data products.
5. How would you handle data product retirement?