1. Can Grafana Connect Directly to Bigtable?
Not directly.
Grafana does not natively support Bigtable as a data source.
To integrate Bigtable into Grafana, you must use an intermediary layer that can:
Query Bigtable
Transform results into time-series format
Expose them through a Grafana-supported API or plugin
Common gateways:
Bigtable → BigQuery → Grafana
Bigtable → Prometheus / OpenTelemetry → Grafana
Bigtable → Custom API (REST/GraphQL) → Grafana SimpleJSON plugin
Bigtable → TimescaleDB / InfluxDB → Grafana
Which one you choose depends on latency needs.
2. Architecture Options for Real-Time Monitoring
Option A: Bigtable → Prometheus → Grafana (Best for real-time < 5s)
How it works
A collector service (Go/Python/Java) polls or streams data from Bigtable.
The collector exposes metrics using the Prometheus exposition format.
Prometheus scrapes the collector.
Grafana dashboards visualize Prometheus metrics.
Pros
Low latency (sub-second to a few seconds)
Ideal for metrics / counters / operational dashboards
Pushes Bigtable data into a standard monitoring pipeline
Cons
Requires a custom collector
Prometheus storage grows fast if metrics are high-cardinality
Option B: Bigtable → BigQuery → Grafana (Best for analytics dashboards)
How it works
Use Dataflow or Bigtable-to-BigQuery connectors to stream data.
Grafana connects directly to BigQuery via the built-in plugin.
Dashboards query BigQuery SQL tables.
Pros
No custom code
Supports SQL analytics and aggregations
Good for historical dashboards
Cons
Not truly “real-time”: typical latency 5–30 seconds
BigQuery is not designed for high-frequency refresh
Option C: Bigtable → Custom REST API → Grafana SimpleJSON Plugin
How it works
Build a backend service:
Reads Bigtable
Applies filters (time range, key prefix, column families)
Returns time-series JSON
Grafana queries it using the SimpleJSON or Infinity plugin.
Pros
Flexible
Good for complex queries that Bigtable supports natively (row keys, timestamps)
Works well if you need live data without Prometheus overhead
Cons
Requires custom development
Must implement caching and rate limiting
Option D: Bigtable → Pub/Sub → Stream Processor → TSDB → Grafana
TSDB options: InfluxDB, TimescaleDB, VictoriaMetrics, ClickHouse.
When to use
If you need real-time ingestion plus a time-series optimized storage layer.
Pros
Fully real-time
High-performance analytics
Perfect for operational dashboards
Cons
Most complex architecture
3. Querying Bigtable for Monitoring
Bigtable excels when data is stored using time-based row keys, such as:
deviceId#timestamp
service#shard#timestamp
region#metric#timestamp
Ideal use cases:
IoT telemetry
Application logs
Event counters
Latency or performance measurements
High-velocity operational metrics
Bigtable operations used:
Prefix scans for specific entities
Timestamp filtering
Column family projections
Row ranges for time windows
Example Go snippet conceptually:
rng := bigtable.NewRange(
"serviceA#20251201090000",
"serviceA#20251201091500",
)
err := tbl.ReadRows(ctx, rng, func(row bigtable.Row) bool {
// Extract timestamps and values, push to exporter
return true
})
4. Latency Considerations
Integration Path Typical Latency Suitable For
Prometheus collector 1–5 seconds Real-time operational dashboards
Custom REST API 0.5–5 seconds Real-time ad-hoc visualizations
BigQuery path 5–60 seconds Analytics / trends
Streaming TSDB ~1 second High-frequency monitoring
Bigtable itself can provide data with single-digit millisecond latency, but Grafana refresh rate, intermediate storage, and query volume affect end-to-end latency.
5. Best Practices for Using Bigtable with Grafana
1. Use time-oriented row keys
Bigtable performs best when data is sorted naturally by time.
2. Pre-aggregate metrics if possible
Grafana doesn’t do heavy time-series aggregation well without TSDB support.
3. Avoid full-table scans
Implement:
Row key prefixes
Time-window ranges
Column pruning
Caching at the API/collector layer
4. Rate-limit Grafana queries
Especially when multiple people open the dashboard.
5. Consider downsampling
For real-time charts, small time windows (1–15min) perform best.
6. Example Architectural Blueprint (Common Use Case)
Scenario:
IoT sensors generate high-frequency temperature/pressure metrics stored in Bigtable. You need a real-time Grafana dashboard.
Architecture
IoT Devices → Pub/Sub → Dataflow → Bigtable
↓
Metrics Exporter → Prometheus → Grafana
Result
Real-time (1–2s) metrics
Long-term, scalable Bigtable storage
Flexible Grafana visualization
7. Summary
Using Bigtable with Grafana requires an intermediate layer. For true real-time monitoring:
Bigtable (storage) → Prometheus (metrics) → Grafana (dashboards)
is the most reliable, low-latency, and widely used architecture.
For analytical dashboards:
Bigtable → BigQuery → Grafana
For custom, flexible, real-time queries:
Bigtable → REST/GraphQL API → Grafana SimpleJSON
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