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Generate Streaming Platform Synthetic Data in Python

Streaming platform data has a critical coherence requirement: churned_at must be null for active subscribers and non-null for churned ones — and is_churned must match. Get this wrong and your churn prediction model trains on contaminated labels. Misata generates a four-table streaming dataset where is_churned and churned_at are always consistent, content types follow realistic catalog distributions (series 55%, movies 35%, documentaries 10%), and watch completion rates match real streaming benchmarks (~65% for movies).

import misata

tables = misata.generate(
    "A Netflix-like streaming service with 10k subscribers and a content library",
    rows=10_000,
    seed=42,
)
print(list(tables.keys()))   # ['subscribers', 'content', 'watch_history', 'ratings']

# churned_at is null for all active subscribers
active = tables["subscribers"][~tables["subscribers"]["is_churned"]]
assert active["churned_at"].isna().all()

What Misata generates

Four tables: subscribers, content, watch_history (linking subscribers to content), and ratings. Churn logic is coherent, content distribution is realistic, and viewing patterns match real streaming behavior.

Tables and columns

Table Key columns
subscribers subscriber_id, name, email, plan, country, joined_at, is_churned, churned_at
content content_id, title, type, genre, release_year, duration_minutes, rating, language
watch_history view_id, subscriber_id, content_id, watched_at, watch_duration_minutes, completed, device
ratings rating_id, subscriber_id, content_id, score, rated_at

Realistic distributions

  • churned_at is null for active subscribers — is_churned and churned_at are always consistent
  • Content type: series 55%, movies 35%, documentaries 10% — matching real platform catalog ratios
  • Watch completion rate ~65% for movies; lower per-episode completion for series
  • Plan distribution across basic, standard, and premium tiers with realistic uptake ratios
  • Device mix across mobile, smart TV, desktop, and tablet — matching real streaming device splits

Quick start

import misata
import pandas as pd

tables = misata.generate(
    "A streaming service with 5k subscribers and a diverse content library",
    rows=5000,
    seed=42,
)

# Churn rate
churn_rate = tables["subscribers"]["is_churned"].mean()
print(f"Churn rate: {churn_rate:.1%}")

# Watch completion by content type
merged = tables["watch_history"].merge(
    tables["content"][["content_id", "type"]], on="content_id"
)
print(merged.groupby("type")["completed"].mean())

# Plan distribution
print(tables["subscribers"]["plan"].value_counts(normalize=True))

Common use cases

  • Recommendation model training — use watch_history and ratings as the interaction matrix for collaborative filtering models before you have real viewing data
  • Churn prediction pipelines — train models on subscriber engagement patterns (watch frequency, completion rates, last active date) against is_churned labels
  • Content performance analytics — build watch time, completion rate, and rating dashboards by genre, type, and release year
  • A/B test framework validation — generate subscriber cohorts with varied plan and country distributions to test experiment assignment pipelines
  • Personalization engine testing — validate recommendation ranking logic and fallback strategies against a full content catalog
  • Subscriber lifecycle QA — test onboarding, upgrade, downgrade, and cancellation workflows against subscribers with realistic join and churn patterns

Advanced: viral growth narrative

tables = misata.generate(
    "Streaming service that gained subscribers rapidly after a viral original series, "
    "now facing increasing churn as the content catalog ages",
    rows=10_000,
    seed=42,
)

Advanced: locale-aware generation

# Latin American streaming — Spanish and Portuguese content, regional subscriber distribution
tables = misata.generate("Latin American streaming platform with Spanish content library", rows=5000)

# Asian streaming — Korean, Japanese, and Chinese content types
tables = misata.generate("Asian streaming platform with K-drama and anime content", rows=5000)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "Streaming platform with 10k subscribers",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds churn risk↔watch_duration correlation
    rows=10_000,
    seed=42,
)