How to build Google Kubernetes Engine dashboards in Metabase

Google Kubernetes Engine is Google Cloud's managed Kubernetes service for running containerized workloads. Metabase is where you turn those operational signals into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the GKE MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Google Kubernetes Engine rollups into a database so you can build dashboards anyone can read.

How do you connect Google Kubernetes Engine to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring reliability reporting to a warehouse-backed model.

What can you analyze from Google Kubernetes Engine data in Metabase?

• Cluster utilization and headroom — built from clusters and workloads and the related node pools, cluster operations, utilization metrics data your sync exposes.
• Cost by cluster and namespace — built from clusters and workloads and the related node pools, cluster operations, utilization metrics data your sync exposes.
• Upgrade and operation health — built from clusters and workloads and the related node pools, cluster operations, utilization metrics data your sync exposes.
• Workload reliability — built from clusters and workloads and the related node pools, cluster operations, utilization metrics data your sync exposes.
• Node pool efficiency — built from clusters and workloads and the related node pools, cluster operations, utilization metrics data your sync exposes.

Which Google Kubernetes Engine dashboards should you build in Metabase?

How do you use the GKE MCP server with the Metabase CLI?

Pair the GKE MCP server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for the current inventory of clusters and workloads with utilization summaries.
• Export the result as CSV, keeping stable IDs, services, environments, severities, and timestamps.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Daily snapshots are required for utilization, capacity, and cost trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Google Kubernetes Engine MCP and the Metabase CLI?

{
  "mcpServers": {
    "gke": {
      "url": "https://container.googleapis.com/mcp"
    }
  }
}

The remote server is read-only inspection (clusters, manifests, logs, operations). An experimental local gke-mcp CLI adds cost analysis and write operations.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a clusters-and-workloads export — creates a table AND a model
mb upload csv --file gke-clusters-and-workloads.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file gke-clusters-and-workloads.csv

Can you generate a Google Kubernetes Engine dashboard with AI?

Yes. Use the prompt below with any assistant that can run the GKE MCP server and the Metabase CLI. It works end to end: if Google Kubernetes Engine tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for Google Kubernetes Engine infrastructure analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering and operations leaders understand utilization, deployment health, cost signals, and workload reliability from Google Kubernetes Engine data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for gke tables and
  models). If durable Google Kubernetes Engine data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the GKE MCP server:
  clusters and workloads, plus node pools, cluster operations, utilization metrics.
  Prefer aggregated or rollup views over raw events. Write each result to a CSV,
  then load it with the Metabase CLI — run "mb upload csv --file <export>.csv" so
  each upload creates a table and a ready-to-query model. Use "mb upload replace
  <table-id> --file <export>.csv" to refresh an existing table instead of creating
  duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, services,
environments, timestamps, and whether rollups or history exist before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Google Kubernetes Engine — it reads a database or CLI-uploaded tables.
- Never try to load the raw event firehose into Metabase; use rollups, entity
  tables, and incident- or group-grain data.
- Only compute durations (MTTA, MTTR, time-to-resolve) when the required
  timestamps exist.
- Exclude test, staging, or muted objects from headline reliability cards, and
  segment by environment where the field exists.
- A single CSV is a point-in-time snapshot: only build trend cards if there is a
  usable date column or multiple periods have been uploaded.

Dashboard title: Google Kubernetes Engine Infrastructure Overview

Sections:
1. Executive summary: Clusters/services tracked; Avg utilization; Deploys last
   30 days; Change failure rate; Cost last 30 days if synced.
2. Utilization: CPU/memory usage vs requests by workload; headroom by cluster.
3. Changes: Deployments and rollbacks by week; failure rate by service.
4. Cost: Spend by service/namespace by month; idle resources.
5. Reliability: Restarts, failed jobs, pending workloads, availability.

Filters: Date range, Service, Environment, Severity, Team, Status.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync Google Kubernetes Engine data into a database or warehouse?

For dashboards that need history and reliability, land Google Kubernetes Engine rollups and metadata in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the Cloud Monitoring API for control over rollup grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Use Google-native paths: enable GKE cost allocation with the Cloud Billing BigQuery export for cost, pull utilization from the Cloud Monitoring API — and point Metabase at BigQuery directly.

Notes

• Decide the rollup grain first (hourly or daily per service/environment) — it drives warehouse cost and every trend card.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize cluster, workload, environment, capacity, usage, and cost-allocation fields.

How should you model Google Kubernetes Engine data in Metabase?

Core tables

Modeling advice

• Build a clean resource_snapshots model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (services, monitors, policies) from time-series rollups and event-grain tables.
• Exclude test, staging, and muted objects from headline reliability metrics; keep environment as an explicit column.
• Use stable IDs for service, team, and incident joins; display names change.

Which Google Kubernetes Engine metrics should you track in Metabase?

What SQL powers Google Kubernetes Engine dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  workload_name,
  ROUND(AVG(cpu_used_cores / NULLIF(cpu_requested_cores, 0)) * 100, 1)
    AS avg_cpu_utilization_pct,
  ROUND(AVG(memory_used_bytes / NULLIF(memory_requested_bytes, 0)) * 100, 1)
    AS avg_memory_utilization_pct
FROM resource_snapshots
WHERE snapshot_date >= CURRENT_DATE - INTERVAL '14 days'
GROUP BY workload_name
ORDER BY avg_cpu_utilization_pct ASC;

SELECT
  date_trunc('week', deployed_at) AS week,
  COUNT(*) AS deployments,
  COUNT(*) FILTER (WHERE status IN ('failed', 'rolled_back')) AS failed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE status IN ('failed', 'rolled_back'))
    / NULLIF(COUNT(*), 0), 1
  ) AS change_failure_rate
FROM deployments
WHERE environment = 'production'
GROUP BY 1
ORDER BY 1;

SELECT
  date_trunc('month', usage_date) AS month,
  service_name,
  ROUND(SUM(cost_usd), 2) AS cost_usd
FROM cost_allocations
GROUP BY 1, 2
ORDER BY 1, cost_usd DESC;

What are common mistakes when analyzing Google Kubernetes Engine in Metabase?

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FAQ