Expert guide for observability, analytics, telemetry, and data pipelines (OpenTelemetry, PostHog, Mixpanel) / Panduan ahli untuk observabilitas, telemetri, dan analitik.
Scanned 9/6/2026
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---
name: data-telemetry-expert
description: "Expert guide for observability, analytics, telemetry, and data pipelines (OpenTelemetry, PostHog, Mixpanel) / Panduan ahli untuk observabilitas, telemetri, dan analitik."
author: "vibes-plug-swarm"
---
# Data & Telemetry Expert (OpenTelemetry 1.x / ClickHouse Edition)
[English](#english) | [Bahasa Indonesia](#bahasa-indonesia)
---
<a name="english"></a>
## English
### Orchestration & Integration
Connects and orchestrates with relevant domain skills like `brainstorming`, `zero-to-prod-orchestrator`, and `project-context-mapper` to ensure cohesive execution.
### Description
Expert guide for production observability, product analytics, and data pipelines. Covers **OpenTelemetry 1.x** (stable, vendor-neutral traces/metrics/logs), **PostHog** (open-source product analytics), **ClickHouse** (OLAP analytics database), Grafana stack, and AI agent observability patterns.
### Trigger Conditions
- Adding distributed tracing to a microservice or Next.js application.
- Setting up structured logging and metrics collection.
- Implementing product analytics (funnel analysis, feature flags, session replay).
- Building a high-performance analytics pipeline with ClickHouse.
- Monitoring AI agent runs, LLM token costs, and response quality.
### OpenTelemetry 1.x — Vendor-Neutral Observability
OpenTelemetry (OTel) is the CNCF standard for generating traces, metrics, and logs from any application.
#### Three Pillars of OTel
| Signal | What It Captures | Example |
|---|---|---|
| **Traces** | Request flow across services | `GET /api/users` → DB query → cache |
| **Metrics** | Numeric measurements over time | `http_requests_total`, `db_query_duration` |
| **Logs** | Structured event records | `{"level":"error","msg":"DB timeout"}` |
#### Next.js 15 + OTel Instrumentation
```typescript
// instrumentation.ts (Next.js built-in OTel support)
export async function register() {
if (process.env.NEXT_RUNTIME === 'nodejs') {
const { NodeSDK } = await import('@opentelemetry/sdk-node');
const { OTLPTraceExporter } = await import('@opentelemetry/exporter-trace-otlp-http');
const { OTLPMetricExporter } = await import('@opentelemetry/exporter-metrics-otlp-http');
const { PeriodicExportingMetricReader } = await import('@opentelemetry/sdk-metrics');
const { Resource } = await import('@opentelemetry/resources');
const { SEMRESATTRS_SERVICE_NAME } = await import('@opentelemetry/semantic-conventions');
const sdk = new NodeSDK({
resource: new Resource({
[SEMRESATTRS_SERVICE_NAME]: 'my-saas-app',
}),
traceExporter: new OTLPTraceExporter({
url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
}),
metricReader: new PeriodicExportingMetricReader({
exporter: new OTLPMetricExporter(),
exportIntervalMillis: 30_000,
}),
});
sdk.start();
}
}
```
#### Custom Spans for Business Logic
```typescript
import { trace, SpanStatusCode } from '@opentelemetry/api';
const tracer = trace.getTracer('my-service', '1.0.0');
async function processOrder(orderId: string) {
return tracer.startActiveSpan('processOrder', async (span) => {
span.setAttribute('order.id', orderId);
span.setAttribute('order.source', 'api');
try {
const order = await db.order.findUnique({ where: { id: orderId } });
span.setAttribute('order.amount', order.amount);
const result = await chargeCustomer(order);
span.setStatus({ code: SpanStatusCode.OK });
return result;
} catch (error) {
span.recordException(error as Error);
span.setStatus({ code: SpanStatusCode.ERROR, message: String(error) });
throw error;
} finally {
span.end();
}
});
}
```
### ClickHouse — High-Performance Analytics Database
ClickHouse is the 2026 standard for analytical workloads — ingests billions of events and queries them in milliseconds:
```sql
-- Create an events table optimized for time-series analytics
CREATE TABLE events (
event_id UUID DEFAULT generateUUIDv4(),
workspace_id String,
user_id String,
event_name LowCardinality(String),
properties JSON,
timestamp DateTime64(3, 'UTC'),
date Date DEFAULT toDate(timestamp)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (workspace_id, event_name, timestamp)
TTL date + INTERVAL 1 YEAR;
-- Query: Funnel analysis — users who signed up then upgraded
SELECT
countIf(event_name = 'signup') AS signups,
countIf(event_name = 'plan_upgraded') AS upgrades,
round(countIf(event_name = 'plan_upgraded') / countIf(event_name = 'signup') * 100, 2) AS conversion_rate
FROM events
WHERE workspace_id = 'ws_abc'
AND timestamp >= now() - INTERVAL 30 DAY;
```
```typescript
// Node.js ClickHouse client
import { createClient } from '@clickhouse/client';
const client = createClient({ url: process.env.CLICKHOUSE_URL });
await client.insert({
table: 'events',
values: [{
workspace_id: 'ws_abc',
user_id: 'user_123',
event_name: 'page_view',
properties: { path: '/dashboard', referrer: 'google.com' },
timestamp: new Date().toISOString(),
}],
format: 'JSONEachRow',
});
```
### PostHog — Open-Source Product Analytics
```typescript
// Next.js + PostHog (client-side)
import posthog from 'posthog-js';
posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
api_host: process.env.NEXT_PUBLIC_POSTHOG_HOST ?? 'https://app.posthog.com',
capture_pageview: false, // Manual with App Router
});
// Track custom events
posthog.capture('feature_used', {
feature: 'ai_assistant',
plan: user.plan,
workspace_id: workspace.id,
});
// Feature flags
if (posthog.isFeatureEnabled('new-dashboard')) {
return <NewDashboard />;
}
```
### AI Agent Observability
Track LLM costs, latency, and quality for production AI applications:
```typescript
// Custom OTel attributes for LLM calls
span.setAttribute('llm.model', 'claude-4-sonnet');
span.setAttribute('llm.input_tokens', response.usage.input_tokens);
span.setAttribute('llm.output_tokens', response.usage.output_tokens);
span.setAttribute('llm.cost_usd', calculateCost(response.usage));
span.setAttribute('llm.latency_ms', Date.now() - startTime);
span.setAttribute('llm.cached', response.usage.cache_read_input_tokens > 0);
```
Backend tracing tools for LLM: **LangSmith** (LangChain/LangGraph), **OpenAI Tracing** (Agents SDK), **Langfuse** (open-source, any LLM).
---
<a name="bahasa-indonesia"></a>
## Bahasa Indonesia
### Integrasi Orkestrasi
Terhubung dan mengorkestrasi skill domain yang relevan seperti `brainstorming`, `zero-to-prod-orchestrator`, dan `project-context-mapper` untuk memastikan eksekusi yang kohesif.
### Deskripsi
Panduan ahli untuk observabilitas produksi, analitik produk, dan pipeline data. Mencakup **OpenTelemetry 1.x** (stabil, vendor-neutral traces/metrics/logs), **PostHog** (analitik produk open-source), **ClickHouse** (database analitik OLAP), dan pola observabilitas agen AI.
### Kondisi Pemicu
- Menambahkan distributed tracing ke microservice atau aplikasi Next.js.
- Menyiapkan structured logging dan pengumpulan metrik.
- Mengimplementasikan analitik produk (analisis funnel, feature flags, session replay).
- Membangun pipeline analitik berkinerja tinggi dengan ClickHouse.
- Memantau run agen AI, biaya token LLM, dan kualitas respons.
### OpenTelemetry 1.x — Observabilitas Vendor-Neutral
Tiga pilar OTel:
- **Traces**: Aliran permintaan antar layanan.
- **Metrics**: Pengukuran numerik dari waktu ke waktu.
- **Logs**: Catatan peristiwa terstruktur.
Integrasikan dengan Next.js 15 melalui file `instrumentation.ts` bawaan — OTel SDK otomatis mendistribusikan trace ke backend pilihan (Grafana Tempo, Jaeger, Honeycomb, Datadog, dll.).
### ClickHouse — Database Analitik Berkinerja Tinggi
ClickHouse adalah standar 2026 untuk workload analitik — menyerap miliaran event dan melakukan query dalam milidetik. Gunakan engine `MergeTree` dengan partisi per bulan dan pengurutan berdasarkan kolom yang sering di-filter.
### PostHog — Analitik Produk Open-Source
PostHog menyediakan analisis funnel, feature flags, session replay, dan A/B testing dalam satu platform yang dapat di-self-host. Integrasikan dengan Next.js App Router menggunakan `posthog-js`.
### Observabilitas Agen AI
Lacak biaya LLM, latensi, dan kualitas untuk aplikasi AI produksi menggunakan custom OTel attributes. Gunakan LangSmith, OpenAI Tracing, atau Langfuse (open-source) sebagai backend tracing LLM.
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