Real-time insights

1) What is "real-time insight"

Insight in real time - a verifiable statement about the current state of the process/user/system, appearing within the target delay (latency) sufficient to make a decision (seconds-minutes).
Loop Formula: Event → Enrichment/Aggregation → Decision/Recommendation → Action → Feedback.

Examples: anti-fraud for transactions (≤500 ms), alert SLO service (≤60 s), personal recommendation on the page (≤200 ms), dynamic pricing (≤5 s), campaign monitoring (≤1 min).

2) Architecture in the palm of your hand

1. Ingest: event broker (Kafka/Pulsar/NATS/MQTT), scheme contracts (Avro/Protobuf), idempotency keys.
2. Streaming (CEP/Stream): Flink/Spark Structured Streaming/ksqlDB; windows, watermarks, stateful operators.
3. Online features and status: Feature Store (online) + cache/TSDB (RocksDB/Redis) for fast join/lookup.
4. Online scoring/rules: models (ONNX/TF-Lite/XGB), rule-engine, context.
5. Surving insights: low-latency API, webhooks, command buses (action bus), adaptive dashboards.
6. NTAP/real-time storefronts: incremental materializations (ClickHouse/Pinot/Druid/Delta + CDC).
7. Observability and SLO: latency/lag/error, trace, alert metrics.
8. Management and security: OTA/feature flags, RLS/CLS, masking, audit.

3) Time model: windows, watermarks, late

Windows: tumbling/sliding/session; for shop windows - a hybrid (1s→5s→60s roll-ups).
Watermark: border after which the window is "closed"; a balance between freshness and fullness.
Late data: acceptance policy 'Δ _ late' (e.g. 2 min), compensation recalculations.
Out-of-order: aggregate by 'event _ time', store 'ingested _ at' for forensics.

4) Exactly-once in meaning and idempotency

• global'event _ id ', idempotency keys tables;
• upsert/merge-sinks;
• state snapshots + transaction commits (2-phase/transaction log);
• deterministic transformations and atomic swap when publishing storefronts.

5) Condition and enrichment

Stateful operators: key-by (user/device/merchant), aggregates, top-K, distinct.
Online join: quick lookup tables (e.g. customer profile, risk limits).
Caching: LRU/TTL, warm features, directory versioning.
Online/offline consistency: a single specification in the Feature Store.

6) Insight ≠ just a metric

Add a decision card to the insider: hypothesis/context → alternative → recommended action → expectations. effect → risk/guardrails → owner/delivery channel.
Zero-click insight: short text + ready-made buttons (applied automatically if low-risk).

7) Anomalies, causality and experiments

Detection: robust z-score/ESD, seasonal-decompose, change-point (CUSUM/BOCPD), sketches (TDigest/HLL) for large flows.
Causality: avoid "noise response" - confirm effect through quasi-experiments/control segments.
Online experiments: bandits/UCB/TS for choosing an action with limited time, guardrail metrics (SLA, complaints, returns).

8) SLO for real-time insights

Latency p95/p99 end-to-end (ingest→deystviye).
Freshness of shop windows (max lag).
Completeness within the window (percentage of late entries).
Action Rate/Success Rate (how many insights turned into action/effect).
Cost-to-Insight (CPU/IO/GPU/$, per 1 insight).

An example of a target matrix: antifrode p95≤300 ms, completeness≥99. 5%, cost/1k sobyty≤$Kh.

9) Delivery of insights and prioritization

Where: webhooks, message bus "actions. , "dashboard API, push/chatbots, CRM/CDP.
Priorities: Gold/Silver/Bronze; Gold - individual pools and channels.
Deadlines: if 'deadline' has expired - demotion or cancellation.

10) Economics and degradation

Cost-aware strategy: simplified models, larger windows, peak sampling.
Graceful degradation: fallback on rough units/rules, "warm" snapshots.
Backpressure & shed-load: reset best-effort themes, keep Gold.

11) Security and privacy

RLS/CLS on stream displays; split by tenant/region.
PII edition at the edge: tokenization to the center.
Secrets and access: mTLS, short tokens, request/export auditing.
Export policies: banning "raw" real-time PII outside without reason.

12) Observability of real-time contour

Lags by topics/keys, queue depth, watermark skew.
p95/p99 on each layer, error rate, reprocess count.
Data-quality online: duplicates, null-rate, distribution anomalies.
Tracing: end-to-end trace-id from event to action.

13) Antipatterns

"Everything is real-time." Unnecessary expense and noise; some tasks are better than batch/near-real-time.
SELECT and "free" schemes without contracts.
Windows without watermarks. Either eternal windows or late losses.
No idempotency. Double action/spam.
Without guardrails. Reacting to a "false positive" creates damage.
OLTP under analytics fire. No isolation - degradation of production transactions.

14) Implementation Roadmap

1. Discovery: events, target solutions, deadlines, risks; classify Gold/Silver/Bronze.
2. Data contracts: schemas (Avro/Protobuf), keys, idempotence policies.
3. MVP stream: one critical solution, window/WM, simple rules + online features.
4. Display cases and serving: incremental materializations, low-latency API.
5. Observability: lag panels/latency/SLO, alerts; tracing.
6. Models and experiments: online scoring, bandits/guardrails.
7. Hardening: backpressure, degradation, cost-profile; audit and privacy.
8. Scale: multi-region, edge analytics, thread prioritization.

15) Pre-release checklist

• SLO (latency, freshness, completeness) and owner are defined.
• Circuits are versioned; 'SELECT'is not allowed; there are idempotency-keys.
• Windows and watermarks configured, late data/recalculation policy.
• Exactly-once in meaning: upsert/merge-sinks, atomic publish.
• Online features are consistent with offline; caches with TTL and versions.
• Guardrails for action; channels are prioritized; deadlines are indicated.
• Lag monitoring/latency/SLO; tracing is enabled; alerts to the SLO threat.
• Privacy policies (RLS/CLS/PII) and export auditing are enabled.
• Runbooks of degradation and incidents are ready (rollback/slow-path).

16) Mini-templates (pseudo-YAML/SQL)

Window/Latecomer Policy

yaml windowing:
type: sliding size: 60s slide: 5s watermark:
lateness: 120s late_data:
accept_until: 90s recompute: true

Idempotent sink (SQL thumbnail)

sql merge into rt_fact as t using incoming as s on t. event_id = s. event_id when not matched then insert (...)
when matched and t. hash <> s. hash then update set...

guardrails rules for action

yaml action_policy:
name: promo_offer_rt constraints:
- metric: churn_risk_score; op: ">="; value: 0. 7
- metric: complaint_rate_24h; op: "<"; value: 0. 02 cooldown_s: 3600 owner: "growth-team"

SLO Alerts

yaml alerts:
- name: e2e_latency_p95 threshold_ms: 1500 for: 5m severity: high
- name: freshness_lag threshold_s: 60 severity: high

17) The bottom line

Real-time insights are not just "quick graphs," but an engineering circuit of solutions: strict event contracts, correct temporal logic (windows/watermarks), idempotent publications, consistent online features, prioritized delivery of actions, and observability with SLOs. When this circuit works, the organization responds in a timely, safe and predictable manner, converting the flow of events into measurable business value.