DataOps-experts

1) What is DataOps and why iGaming

DataOps is a set of engineering, product and operational practices that make the flow of data predictable, fast and secure, from sources and contracts to storefronts, BI and ML.
In iGaming, the stakes are high: regulatory (KYC/AML/RG), real-time money, marketing experiments, frequent releases of game providers and PSPs.

• Shorten the "idea → data → metric/model" loop.
• Stable quality and reproducibility.
• Controlled changes (rollout/rollback).
• Transparency: who is responsible for what, where it "breaks."

2) Value Stream

1. Source/Contract → 2) Ingestion → 3) Bronze/Silver/Gold → 4) Feature Store/BI → 5) Consumers (Product, Analytics, ML) → 6) Feedback.

At each stage - artifacts, tests, metrics, owners and SLOs.

3) Contract-oriented data development

Data Contracts: scheme, types, mandatory, allowed values, SLA freshness/delivery, DQ rules, privacy ('pii', 'tokenized').
Compatibility (SEMVER): MINOR - additions, MAJOR - incompatibility, PATCH - fixes.
CI-gates: we block PR if the contract breaks/no tests/retension.
Data agreements with providers/PSP/KYC: formats, signature, retrays, deduplication.

4) Data testing (before/during/after)

Before (design): contract tests, sample sets, data generators.

• Schema tests (type/nullable/enum/compatibility),
• DQ tests (validity, uniqueness, completeness, freshness),
• Privacy rules (Zero-PII in logs/storefronts),
• Idempotency checks and dedup.
• After (acceptance): window regression tests/feature, comparison v1/v2 (tolerance bands), calibration of metrics.

5) Orchestration and environments

Orchestrator (Airflow/eq.) as a source of truth about runs: addictions, retreats, SLAs, alerts.
Environments: dev → stage → prod with promotion of artifacts (tables, models, feature network).
Isolation by brand/region/tenant: separate schemes/directories/encryption keys.
Release flags and configuration as data for non-relogue switches.

6) Releases and deployment strategies

Blue-Green/Canary for storefronts and models: v2 parallel assembly, comparison, partial traffic.
Dual-write/dual-read on schema migrations.
Feature flags on low load and reversibility.
Backfill playbooks: reloading history, checksums, 'recomputed' labels.

7) Observability and alerts (Data Observability)

Freshness/completeness/volumes/anomalies by lineage nodes.
Quality: pass-rate DQ, red paths for KPIs.
Schemes/Contracts: incompatibility events,% successfully passed checks.
Performance: pipeline latency, cost (compute/storage).

Interpretability: links "istochnik→vitrina/model," fast "path to dashboard/KPI."

8) Incident management

Sev-levels (P1-P3), RACI, communication channels.
Runbooks: common causes (source missing, schema drift, key leak, fraud noise).
Auto-mitigation: retrai, switching to a spare channel, "freezing" shop windows.
Post-mortem: the root of the problem, actions, prevention tasks in the backlog.

9) Security, privacy and access in DataOps

mTLS/TLS 1. 3, packet signature, party hashes.

Tokenization/masking in storefronts and logs; detokenization only in the "clean zone."

RBAC/ABAC/JIT with audit; break-glass for incidents.
Retention/Legal Hold agreed with pipelines (TTL, lifecycle).
Zero-PII in the logs is the partition metric.

10) BI/ML as full-fledged DataOps consumers

BI: certification of "gold" showcases, prohibition of 'SELECT', versioning of KPI definitions.
ML: Feature Store with versions, registry models, champion-challenger, fairness/privacy gates, counterfactual tests.

11) Success Metrics (SLO/SLI)

• Freshness SLO (e.g. payments_gold ≤ 15 min, p95).
• Job Success Rate ≥ 99. 5%, Mean Time to Detect (MTTD) / Recover (MTTR).
• Lead Time for Change (ideya→prod), Deployment Frequency (releases/week).

• DQ Pass-Rate ≥ the target threshold (over critical paths).
• Schema Compatibility Pass в CI.
• Delta v1/v2 in tolerances.

• Zero-PII in logs ≥ 99. 99%.
• Detokenization SLO and 100% audit.
• Retention On-time Deletion ≥ the target threshold.

• Time of report/showcase publication.
• Reduction of data incidents, impact on KPIs (GGR, retention) within control.

12) Templates (ready to use)

12. 1 Data Contract (fragment)

yaml name: game_rounds_ingest owner: games-domain schema_version: 1. 6. 0 fields:
- name: round_id type: string required: true
- name: bet_amount type: decimal(18,2)
required: true dq_rules:
- rule: bet_amount >= 0
- rule: not_null(round_id)
privacy:
pii: false tokenized: true sla:
freshness: PT15M completeness: ">=99. 9%"
retention: P12M

12. 2 PR Checklist for Display/Feature

• Updated contract/scheme, semver correct
• DQ/schema/regression tests are green
• Release Notes + Linejay Impact
• backfill/rollback plan ready
• Threshold alerts and dashboards configured
• Privacy/access policies are followed

12. 3 Release Notes

What: 'rg _ signals v1. 3. 0 '- added' loss _ streak _ 7d'

Type: MINOR, scheme compatible

Impact: BI'rg _ dashboard ', ML'rg _ model @ 2. x`

Validation: dual-run 14 days, delta ≤ 0. 3% on key KPIs

Rollback: flag'rg _ signals. use_v1=true`

Owner/Date/Ticket

12. 4 Runbook ("payment delay" incident)

1. Check PSP source SLA, connector status.
2. Retrai/switch to spare endpoint.
3. Temporary degradation: we publish aggregates without detail.
4. Communication in # data-status, ticket in Incident Mgmt.
5. Post-mortem, RCA, prevention (quotas/cache/control schemes).

13) Roles and Responsibilities (RACI)

CDO/Data Governance Council - Policy, Standards (A/R).
Domain Owners/Data Stewards - Contracts, Quality, Storefronts (R).
Data Platform/Eng - orchestrator, storage, CI/CD, observability (R).
Analytics/BI Lead - showcase certification, KPI definitions (R).
ML Lead - feature store, registry, model monitoring (R).
Security/DPO - privacy, tokenization, access, retention (A/R).
SRE/SecOps - Incidents, DR/BCP, SIEM/SOAR (R).

14) Implementation Roadmap

0-30 days (MVP)

1. Identify critical paths (payments, game_rounds, KYC, RG).
2. Enter contracts and CI-gates (schemes, DQ, privacy).
3. Include observability: freshness/completeness/anomalies + alerts.
4. Gold showcases: fix KPI and ban 'SELECT'.
5. Runbooks and # data-status channel, Release Notes template.

30-90 days

1. Dual-run and canary window/model releases; backfill playbooks.
2. Feature Store/Model Registry with versioning.
3. Access policies (RBAC/ABAC/JIT) and Zero-PII in logs.
4. Dashboards SLO/cost, automation retenschna/TTL.
5. Training of DataOps teams (onboarding, workshops).

3-6 months

1. Full cycle champion-challenger models, fairness/privacy-gates.
2. Geo/tenant isolation, keys and data by jurisdiction.
3. Automatic Release Notes from lineage and diff.
4. Regular post-mortems and quarterly DataOps reviews.
5. External audit of processes (where required by license).

15) Anti-patterns

"We will correct the data later": releases without tests/contracts.
Opaque pipelines: no lineage and no owners.
Manual uploads "bypassing" DataOps processes.
Logs from PII, dumps of production bases in sandboxes.
No rollback/backfill plan.
KPIs without versions and fixed definitions.

16) Related Sections

Data Management, Data Origin and Path, Auditing and Versioning, Access Control, Security and Encryption, Data Tokenization, Model Monitoring, Retention Policies, Data Ethics.

Total

DataOps turns disparate scripts and analyst "heroism" into a manageable production pipeline of data: change is fast but predictable; quality and privacy are monitored; releases are reversible; metrics and models are reproducible. This is the foundation of a scalable iGaming platform.