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Zalingo Synthetic SaaS/App Product Usage & Telemetry — 100k
Synthetic Tabular Data
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£249
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Zalingo Synthetic SaaS/App Product Usage & Telemetry — 100k Sample (Parquet)
A 100,000-row privacy-safe synthetic product analytics & telemetry dataset covering web, mobile, and API event streams. It mimics realistic customer/account behavior (sessions, feature usage, requests, errors, performance, plan tiers) with a stable schema—no PII and not derived from real users. Ideal for feature prototyping, pipeline QA, model baselines, A/B demos, and reliability analytics without access hurdles.
Need larger volumes or scheduled refresh? After purchasing this sample, message us about enterprise-scale bundles and monthly/weekly/daily subscriptions.
Dataset Features (representative)
- event_id — Synthetic unique event ID.
- ts_utc — Event timestamp (ISO-8601, UTC).
- user_id / session_id / account_id — Synthetic identifiers (non-linkable).
- app_surface — web | mobile | api | backend.
- event_name — login, view_page, click_button, feature_use, api_call, error, etc.
- feature_key — Feature/flag identifier when applicable.
- api_endpoint / http_method / status_code — For API events.
- latency_ms / bytes_in / bytes_out — Performance & payload hints.
- device_os / app_version / browser — Client context (when web/mobile).
- country / city — ISO-2 + synthetic city.
- utm_source / utm_campaign / referrer — Synthetic attribution fields.
- plan_tier — free | trial | pro | enterprise.
- mrr_usd — Synthetic monthly recurring revenue at account snapshot.
- active_minutes / session_duration_s / pages_viewed — Engagement metrics.
- error_code / error_class — For error events (short codes).
- churn_risk_score — 0–1 synthetic signal for modelling demos.
- nps_score — Optional 0–10 survey events.
(Exact columns may vary slightly by bundle; see the included preview CSV for this sample’s schema.)
Distribution
- Format: ZIP containing Parquet shards (Snappy) + README.
- Volume: 100,000 rows, ~18–30 columns, 1–5 Parquet parts.
- Approx Size: ~3–5 MB zipped (category-dependent).
- Schema stability: Names/types consistent across technology samples; full datasets partition by date / app_surface / account_id.
Usage
- Growth & product analytics: funnels, cohorts, feature adoption, retention.
- A/B testing sandboxes: flag/feature experimentation without PII.
- Reliability & SRE analytics: error rates, latency SLOs, capacity signals.
- Churn/upsell modelling: plan tiers, engagement, synthetic MRR signals.
- Pipeline QA & MLOps: schema checks, drift tests, dashboard demos.
Coverage
- Geographic: Multi-region synthetic coverage with ISO country codes.
- Time Range: Recent multi-year synthetic window reflecting weekly/seasonal patterns.
- PII: None. Fully synthetic; not re-identifiable.
License
Proprietary — internal use rights; redistribution/resale not permitted.
Who Can Use It
- Data Scientists/ML Engineers — feature engineering, baselines, monitoring.
- Product/Growth/Analytics — funnels, cohorts, activation & retention.
- SRE/Platform — performance/error analysis and alerting logic.
- RevOps/Finance — synthetic MRR & plan-tier experiments.
Important Notes / Disclaimers
- Not real user data. Not for direct targeting of individuals.
- Event rates, errors, and revenue fields follow synthetic distributions calibrated to public benchmarks; they do not represent any specific company.
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