Building Typed Real‑Time Analytics for Warehouses with ClickHouse and TypeScript

Hook: Why automation hardware is faster, but teams still lose hours despite automation — and how TypeScript + ClickHouse fixes that

Warehouse leaders tell the same story in 2026: automation hardware is faster, but teams still chase exceptions, late KPIs, and noisy sensors. The problem is rarely the robot — it's the data pipeline. When sensor streams, WMS events, and worker inputs are loosely typed, delayed, and siloed, you can't run fast, predictable operations.

This article shows a pragmatic, production-ready approach to building typed, real-time analytics for warehouses using ClickHouse and TypeScript. You'll get concrete patterns for typed ingestion from IoT/robot fleets, real-time KPI computation, alerting, and full-stack dashboard integrations (Node, Next.js/React, and Vue) tailored for 2026 warehouse automation trends.

Executive summary: The 2026 playbook in one paragraph

In 2026 warehouses move from isolated automation islands to integrated, data-driven operations. Architect your analytics stack to accept typed telemetry (Protobuf/JSON + runtime validation), stream through Kafka (or MQTT→Kafka bridge), ingest into ClickHouse using the Kafka engine and materialized views for low-latency OLAP. Use TypeScript end-to-end (runtime validators like zod or protobuf codegen) so ingestion contracts are guaranteed. Build dashboards and alerts with Next.js/React or Vue that consume SSE/WebSocket APIs backed by efficient ClickHouse queries and pre-aggregated MergeTree tables. Follow partitioning, ORDER BY, and TTL best practices to keep storage and query costs predictable.

Why ClickHouse + TypeScript is a natural fit for warehouses in 2026

ClickHouse's 2025–26 momentum (including major funding rounds and ecosystem expansion) makes it a go-to OLAP choice for real-time operational analytics. It offers:

• High-throughput inserts — ideal for thousands of sensors and robots emitting events per minute.
• Low-latency queries — SELECTs on pre-aggregated data return in milliseconds for dashboards and alerts.
• Streaming integrations — native Kafka engine, materialized views, and buffer tables for robust ingestion.

TypeScript brings strong compile-time guarantees for your ingestion schema, client libraries, and UI. Combined with runtime validators (zod, io-ts, or Protobuf-generated decoders), you get both safety and speed — crucial when automated forklifts and conveyor belts rely on those metrics.

Architectural overview (high level)

A resilient, typed real-time analytics pipeline for a warehouse looks like this:

1. Robots / PLCs / IoT → MQTT or native TCP → streaming backbone (Kafka).
2. Type-safe ingestion service (Node + TypeScript) with runtime validation → push to Kafka topics.
3. ClickHouse Kafka engine consumes topics → materialized views write into MergeTree tables and pre-aggregated KPI tables.
4. Alerting service (TypeScript) queries ClickHouse or subscribes to KPI streams → Slack/Email/PagerDuty.
5. Dashboard (Next.js/React or Vue) reads KPI tables via API routes or SSE/WebSocket for live updates.

Step 1 — Define typed telemetry contracts

Start by defining the event contracts that robots and automation systems emit. Keep two things in sync: the TypeScript type and a runtime validator. For JSON-based telemetry, zod is a pragmatic choice. For higher performance or versioning, use Protobuf and generate TypeScript types (ts-proto).

Example: zod schema for robot telemetry

import { z } from 'zod'

export const RobotTelemetry = z.object({
  robotId: z.string(),
  timestamp: z.string().transform(s => new Date(s)),
  location: z.object({ x: z.number(), y: z.number(), z: z.number().optional() }).optional(),
  batteryPct: z.number().min(0).max(100),
  taskId: z.string().nullable(),
  state: z.enum(['idle','moving','loading','error']),
  metrics: z.record(z.string(), z.number()).optional(),
})

export type RobotTelemetry = z.infer<typeof RobotTelemetry>

Key takeaways:

• Keep fields explicit and fail-fast on unexpected shapes.
• Use timestamps as ISO strings and convert to Date on validation to avoid inconsistent formats.
• Keep a small set of required fields and allow an extensible metrics map for vendor-specific telemetry.

Step 2 — Typed ingestion service (Node + TypeScript)

The ingestion service performs three responsibilities: validate, enrich, and write to the streaming backbone. Use batch inserts where possible. Below is a simplified Node example that validates telemetry and publishes to Kafka.

Node + Kafka ingestion snippet (TypeScript)

import { Kafka } from 'kafkajs'
import { RobotTelemetry } from './schemas/robot-telemetry'

const kafka = new Kafka({ brokers: ['kafka:9092'] })
const producer = kafka.producer()

async function start() {
  await producer.connect()
}

async function handleRawEvent(raw: unknown) {
  const parsed = RobotTelemetry.safeParse(raw)
  if (!parsed.success) {
    // log & route to DLQ
    console.error('Invalid payload', parsed.error.format())
    return
  }

  const payload = JSON.stringify({ ...parsed.data, timestamp: parsed.data.timestamp.toISOString() })
  await producer.send({ topic: 'robot.telemetry', messages: [{ value: payload }] })
}

Production tips:

• Batch Kafka sends to reduce network overhead.
• Use a dead-letter queue (DLQ) for invalid events and monitor DLQ growth.
• Prefer gzipped Avro/Protobuf for high-throughput scenarios and strong schema evolution guarantees.

Step 3 — ClickHouse ingestion: Kafka engine + materialized views

ClickHouse can consume Kafka topics directly. Use the Kafka engine to read raw JSON or Avro messages and materialized views to transform and write into MergeTree tables optimized for queries.

ClickHouse DDL example

-- Create a table that reads from Kafka
CREATE TABLE robot_telemetry_kafka (
  robotId String,
  timestamp DateTime64(3),
  x Float32 DEFAULT 0,
  y Float32 DEFAULT 0,
  batteryPct Float32,
  taskId Nullable(String),
  state String,
  metrics String -- store JSON metrics in a column if needed
) ENGINE = Kafka SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic_list = 'robot.telemetry', kafka_group_name = 'ch-ingest', kafka_format = 'JSONEachRow';

-- Buffer table + materialized view to main MergeTree
CREATE TABLE robot_telemetry (
  robotId String,
  timestamp DateTime64(3),
  x Float32,
  y Float32,
  batteryPct Float32,
  taskId Nullable(String),
  state String,
  metrics String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (robotId, timestamp)
TTL timestamp + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

CREATE MATERIALIZED VIEW robot_telemetry_mv TO robot_telemetry AS
SELECT
  robotId,
  timestamp,
  x,
  y,
  batteryPct,
  taskId,
  state,
  metrics
FROM robot_telemetry_kafka;

Notes:

• ORDER BY is critical: choose keys that support your query patterns (e.g., robotId + timestamp for per-robot time series).
• Partitioning by month keeps deletes and TTLs efficient.
• Store raw metrics JSON if you need flexible fields, but prefer typed columns for frequent queries.

Step 4 — Pre-aggregate KPIs using materialized views

For sub-second dashboards and alerting, compute KPI rollups as data arrives. Materialized views can maintain running aggregates by minute, hour, or shift.

Example KPI: per-robot uptime and average battery by minute

CREATE TABLE kpi_robot_minute (
  minute DateTime64(0),
  robotId String,
  avgBattery Float32,
  uptimeSeconds UInt32
) ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(minute)
ORDER BY (robotId, minute);

CREATE MATERIALIZED VIEW kpi_robot_minute_mv TO kpi_robot_minute AS
SELECT
  toStartOfMinute(timestamp) AS minute,
  robotId,
  avg(batteryPct) AS avgBattery,
  sumIf(1, state != 'idle') AS uptimeSeconds
FROM robot_telemetry
GROUP BY minute, robotId;

With this pre-aggregated table you can quickly surface KPIs across the fleet and trigger alerting logic without scanning raw telemetry.

Step 5 — Implement typed alerting in TypeScript

Alerts are business logic: battery below threshold for X minutes, anomaly in movement, or throughput dropping. Implement the alerting engine as a scheduled TypeScript service that queries ClickHouse KPI tables, evaluates rules, and dispatches notifications.

Alerting example: battery low

import { ClickHouse } from 'clickhouse'

const ch = new ClickHouse({ url: 'http://clickhouse:8123', debug: false })

async function checkLowBattery() {
  const query = `
    SELECT robotId, AVG(avgBattery) AS avgBattery
    FROM kpi_robot_minute
    WHERE minute >= now() - INTERVAL 15 MINUTE
    GROUP BY robotId
    HAVING avgBattery < 20
  `
  const rows = await ch.query(query).toPromise()
  for (const r of rows) {
    // dispatch alert (Slack/Email/PagerDuty)
    console.log('ALERT: low battery', r.robotId, r.avgBattery)
  }
}

Production recommendations:

• Debounce and aggregate alerts to avoid alert fatigue (e.g., require 2 consecutive intervals).
• Keep alert rules in version-controlled config and test them in a dry-run mode.
• Correlate with event metadata (shift, zone, firmware) to reduce false positives.

Step 6 — Full-stack dashboards: Next.js/React example (TypeScript)

For real-time dashboards use Server-Sent Events (SSE) or WebSocket APIs that push KPI updates from a middle tier querying ClickHouse. Next.js API routes make it simple to implement SSE in TypeScript.

Next.js API route that streams KPI updates (SSE)

// pages/api/kpi/stream.ts
import type { NextApiRequest, NextApiResponse } from 'next'
import { ClickHouse } from 'clickhouse'

const ch = new ClickHouse({ url: process.env.CLICKHOUSE_URL })

export default async function handler(req: NextApiRequest, res: NextApiResponse) {
  res.setHeader('Content-Type', 'text/event-stream')
  res.setHeader('Cache-Control', 'no-cache')
  res.setHeader('Connection', 'keep-alive')

  const send = (data: any) => res.write(`data: ${JSON.stringify(data)}\n\n`)

  // Send an initial snapshot
  const snapshot = await ch.query('SELECT * FROM kpi_robot_minute ORDER BY minute DESC LIMIT 100').toPromise()
  send({ type: 'snapshot', data: snapshot })

  // Poll every 2s for new KPIs (or subscribe to a push mechanism)
  const interval = setInterval(async () => {
    const rows = await ch.query('SELECT * FROM kpi_robot_minute WHERE minute >= now() - INTERVAL 1 MINUTE').toPromise()
    if (rows.length) send({ type: 'update', data: rows })
  }, 2000)

  req.on('close', () => { clearInterval(interval); res.end(); })
}

On the client, use an EventSource to receive updates and render them with React (or Vue). Because the API is typed on the server, you can create matching TypeScript types in the client to avoid guesswork.

Vue integration: lightweight fleet map

If you prefer Vue, the same SSE approach works with a small composable to handle the stream. Use typed interfaces (shared package) so your components know the shape of KPI messages at compile-time.

Operational best practices

• Testing & contracts: Keep telemetry schemas in a shared package, version them, and run contract tests between device firmware teams and the ingestion service.
• Backpressure and batching: Buffer messages on the ingestion service when ClickHouse is under heavy load. Kafka helps absorb spikes.
• Efficient storage: Use compact numeric types, appropriate compression (ZSTD), and TTL to manage retention and costs.
• Observability: Monitor ClickHouse metrics (query latency, insert rate), Kafka lag, and DLQ sizes. Instrument ingestion services with traces.
• Schema evolution: Use Protobuf or Avro for strict backward/forward compatibility; use zod with optional fields for JSON if you need agility.

Scaling patterns and cost controls

ClickHouse scales horizontally but storage and compute must be tuned for your query patterns. For warehouses with many sensors per square meter:

• Use TTL to expire high-cardinality raw telemetry after 7–30 days and keep long-term aggregates only.
• Pre-compute rollups at minute/hour/shift granularity to limit ad-hoc query costs.
• Shard by warehouse or region if you operate multiple facilities to reduce cross-warehouse hotspots.

Security and compliance

Protect operational data and ensure role-based access:

• Use network-level isolation between automation, Kafka, and ClickHouse.
• Enable TLS for ClickHouse HTTP and Kafka clients.
• Implement RBAC on dashboards and redact PII at ingestion time.

2026 trends and why this matters now

“Automation strategies are evolving beyond standalone systems to more integrated, data-driven approaches.” — Warehouse automation playbook, 2026

Two quick context items for 2026:

• Warehouse automation is moving from siloed devices to tightly integrated systems where analytics and operational systems are looped together (Connors Group, 2026).
• ClickHouse's market momentum and large funding rounds in 2025–26 have accelerated ecosystem tooling (connectors, cloud services, observability), making it easier than ever to deploy real-time OLAP for operations.

That means building typed, reliable pipelines is both possible and essential: you can stop firefighting and begin optimizing labor, throughput, and robot utilization in real time.

Advanced strategies and future-proofing

Once you have the basics, try these advanced techniques:

• Feature stores in ClickHouse — store pre-computed features for ML anomaly detection and use the same KPI tables your dashboards use to power predictive maintenance.
• Cross-facility joins — maintain global KPIs by federating ClickHouse clusters and using distributed tables for cross-warehouse analytics.
• Edge aggregation — push minute-level aggregation to edge gateways to reduce central ingest volumes for extremely high-frequency sensors.
• Typed event replay — store validated raw events in ClickHouse (or object storage) and provide a typed replay API for debugging and simulations.

Checklist: Build your first typed real-time warehouse analytics pipeline

1. Define telemetry schemas and runtime validators (zod/Protobuf).
2. Implement an ingestion service that validates, enriches, and publishes to Kafka (batching).
3. Set up ClickHouse Kafka engine + materialized views → MergeTree tables with sensible ORDER BY and TTL.
4. Create materialized KPI rollups (minute/hour/shift) and SummingMergeTree tables for fast queries.
5. Build an alerting service in TypeScript that queries KPIs and debounces alerts.
6. Ship a dashboard with Next.js/React or Vue that consumes SSE/WebSocket updates.
7. Monitor DLQ, Kafka lag, ClickHouse query latency, and cost metrics.

Real-world example: small proof-of-concept plan (2 weeks)

Week 1: Define schemas, build a simple ingestion service that validates and writes synthetic telemetry to Kafka, and configure ClickHouse to consume and store the raw events.

Week 2: Add a minute-level KPI materialized view, implement a basic alert (low battery), and ship a minimal Next.js dashboard that streams KPI updates. Measure latency end-to-end and iterate on batching/partitioning.

Final takeaways

Warehouses in 2026 benefit most when automation hardware and analytics are treated as a single system. ClickHouse provides the speed and scale for real-time OLAP, while TypeScript guarantees that your ingestion contracts and front-end code are aligned. Build typed ingestion, use ClickHouse materialized views for KPIs, and implement pragmatic alerting to move from reactive firefighting to proactive optimization.

Call to action

Ready to prototype? Start with the telemetry schema and a small Kafka + ClickHouse dev environment. If you want a checklist and starter repo with TypeScript schemas, Node ingestion, ClickHouse DDL, and Next.js demo pages, download our 2-week POC kit or join our workshop on designing tomorrow's warehouse analytics in 2026.

Get the starter kit, sample code, and live demo: sign up for the repo and walkthrough at typescript.page/warehouse-clickhouse-2026 (or contact us for a tailored 2-week POC).

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