From Many to One: Best Practices for Building Grafana Dashboards with Multiple Data Sources

Grafana dashboards make it easy to visualize and correlate data from many systems in one place. Whether you’re tracking infrastructure metrics alongside application logs, or blending business KPIs with geospatial insights, multi-source dashboards help you troubleshoot faster and make better decisions.

If you’re getting started, you might be wondering: How do I combine multiple queries in one panel? How do I join data that comes from different databases or formats? And how do I do this without slowing everything down?

This guide shows you how to work with multiple data sources in Grafana, explains the building blocks (queries, data frames, and transformations), and shares practical best practices to create fast, reliable, multi-source dashboards.

How Grafana Turns Raw Data into Visuals

Before we combine data, it’s helpful to understand Grafana’s pipeline:

• Data source plugin: Connects Grafana to systems like Prometheus, PostgreSQL, Elasticsearch, BigQuery, and more than 150 other sources and plugins.
• Query: Fetches data from that source (for example, SQL, PromQL, or a REST request).
• Data frame: Grafana’s internal, intermediate format that captures rows, fields, and types.
• Transformations (optional): SQL-like operations that reshape and combine one or more data frames.
• Visualization: Panels (time series, table, geomap, bar chart, etc.) that render the results.

You can think of transformations as a safe workspace: they rearrange and combine what’s already been queried, without changing the underlying data.

Use Multiple Queries in a Single Panel

You’re not limited to one query per panel. You can add as many as you need and decide how those results should be presented.

Step-by-step:

1. Edit a panel and open the Queries section.
2. Create your first query (Query A).
3. Click Add query to create additional queries (Query B, C, etc.).
4. Give each query a clear name or alias, so you can reference them in transformations and legend labels.

What happens next depends on the visualization:

• Some visualizations (like Time series) naturally display multiple results at once (multiple lines/bars).
• Others (like Table) may show one data frame at a time unless you consolidate them with transformations.

Tip: Use meaningful query names and aliases (for example, “api_error_rate” instead of “A”). Better labels make transformations and legends much easier to understand.

Combine Queries with Transformations

Transformations let you merge, join, filter, and reshape your data frames. Two of the most common operations for multi-source dashboards are Merge (union) and Join.

Merge (Union) When Schemas Match

Use Merge when your data frames have the same columns and you simply want to stack the rows together.

Example scenario:

• Two CSV or SQL queries returning columns: time, region, requests.
• You want one table or time series containing the union of both.

How to do it:

• Go to the Transformations tab.
• Add transformation: Merge.
• Choose the frames to merge (Grafana merges by matching columns).

When to use:

• Appending identical datasets (for example, monthly partitions, per-region slices with the same schema).

Join by Field for SQL‑Style Joins

Use Join by field when schemas differ but you share a key (like id, host, iso_country, or service_name).

Example scenario:

• Query A: Product metadata (id, product_name, owner).
• Query B: Product release info (id, release_year).
• Desired result: One unified table keyed by id.

How to do it:

• Add transformation: Join by field.
• Select the join key (for example, id).
• Choose the join type: inner, left, right, or outer.

Notes:

• Grafana may qualify column names (for example, “product_name (A)”). If you prefer clean labels, follow the join with Organize fields by name to rename, reorder, or hide fields.
• If the fields you want to join on don’t share the same name, alias or rename them first (via query aliasing or the Organize fields transformation).

Other Helpful Transformations

• Filter data by value: Keep only rows that match certain criteria (for example, status = 'error' or airport_type = 'large_airport').
• Convert field type: Fix strings vs numbers or convert epoch to time.
• Group by: Aggregate rows (for example, sum by region or average by service).
• Reduce: Collapse series or tables into single values for Stat panels (for example, last, max, 95th percentile).
• Series to rows / Labels to fields: Reshape results for table-friendly layouts.

Transformation order matters. Think of it like a pipeline: rename/alias first, then join, then filter/organize, then aggregate.

Mix Data Sources (and Formats) in One Panel

Grafana can combine results from multiple data sources—even when they return different formats (JSON, CSV, SQL table results). The Mixed data source lets you add queries from different sources in one panel and then join or merge the results using transformations.

Example walkthrough:

1. Set the panel’s data source to Mixed.
2. Create Query A against a REST/JSON or CSV source (for example, a list of countries with a two-letter code field). Alias Code2 to iso to make it a clean join key.
3. Create Query B using a plugin that reads JSON/CSV from the web (for example, the Infinity data source plugin) to fetch airports, including an iso_country field.
4. Join by field iso to combine country data with airport data.
5. Filter to large airports and switch the visualization to Geomap, mapping latitude and longitude fields.

Result: A single map panel showing the world’s largest airports in the countries returned by Query A.

Important:

• Mixed doesn’t mean unlimited. Large joins across remote APIs can be slow. Filter and pre-aggregate as much as possible at the source.
• If your queries return different key names (iso vs iso_country), use alias/rename to match them before the join.

Performance and Scalability Best Practices

Multi-source dashboards are powerful, but performance can degrade if you try to move too much data into Grafana and then reshape it. Use these tips to keep your dashboards fast and reliable:

• Push work down: Filter and aggregate at the source. Pull only the fields and time range you need.
• Use $__timeFilter() and $__interval macros (where supported) to align time windows and sampling with the dashboard’s time range.
• Limit cardinality and rows: Add WHERE clauses, LIMIT, and GROUP BY at the query level. In transformations, use Filter and Reduce to trim results before they hit the visualization.
• Align time zones and timestamp types: Ensure you’re comparing the same time zone across sources. Convert epoch values to time fields if needed.
• Avoid fan-out joins: If the join key isn’t unique on either side, you may multiply rows unintentionally. De-duplicate keys or aggregate before joining.
• Choose the right join type: Inner joins drop non-matching rows; left/right/outer joins keep them. For sparse time series, use outer joins and handle nulls intentionally.
• Be careful with very large web datasets: For public JSON/CSV endpoints, prefer a service layer that supports filtering and paging. Plugins like Infinity are great, but avoid fetching everything and filtering in Grafana.
• Use Explore and Query Inspector to debug: Inspect raw data frames, query timings, and payload sizes to find bottlenecks.
• Keep alerts simple: Alerting engines are optimized for single-source, directly queryable metrics. Avoid relying on complex panel transformations for alerts; instead, compute a single, alert-ready metric at the source or via expressions designed for alerts.

Modeling Keys and Schemas for Successful Joins

Clean joins start with clean keys. Establish conventions before you build panels:

• Standardize key names: Decide on service_id, iso, hostname, etc., and stick to it across systems.
• Normalize casing and format: For example, always uppercase ISO codes or trim whitespace from IDs.
• Cast types: Ensure both sides use the same type (string vs int).
• Derive keys as needed: Use Organize fields by name (rename), Convert field type, or Add field from calculation to derive a join key when sources don’t provide one natively.

Building Panels That Teams Can Trust

Clear, consistent panels make multi-source dashboards more usable:

• Use meaningful field labels and units: Configure units (ms, %), thresholds, and value mappings so values are self-explanatory.
• Document your panel: Add a concise description explaining the data sources, join keys, and filters. Future you (and teammates) will thank you.
• Name queries descriptively: api_latency_p95, orders_per_min, product_meta.
• Keep transformations readable: Fewer, clearer transformations are easier to maintain than a long chain of tiny steps.
• Preview intermediate steps: Temporarily disable transformations to isolate problems.

Variables, Interactivity, and Reuse

Variables make dashboards dynamic and reusable across teams and environments:

• Data source variables: Let viewers switch between clusters or environments (prod/stage/dev) that share the same schema.
• Query variables: Populate lists like team, region, or service from your data sources; use these variables to parameterize queries.
• Chained variables: Filter one variable based on another (for example, services filtered by selected team).
• Dashboard links and drilldowns: Create contextual links to Explore or related dashboards with variable context carried through.

Tip: When combining sources, make sure variable values (like region names) match across systems or add a mapping layer in a table or transformation.

Security, Governance, and Plugins

Multi-source often means multi‑team and multi‑permission:

• Scope access carefully: Use folders, roles, and data source permissions to control who can view or edit dashboards and data.
• Manage secrets securely: Keep API keys, tokens, and credentials in data source configuration, not in panel text or query bodies.
• Vet plugins: Only install trusted data source plugins. Review what data they access and how they authenticate.
• Version control dashboards: Export JSON models or use provisioning to track changes and roll back if needed.

Real‑World Patterns You Can Reuse

• Operational + business KPI: Join hourly order volume from SQL with API latency from Prometheus by time bucket to see how performance affects conversion.
• Infra + ownership: Join host metrics with CMDB/asset records in PostgreSQL on hostname to highlight which teams own the noisiest machines.
• Geospatial status board: Merge incident data (JSON API) with site latitude/longitude (CSV) and visualize on a Geomap to track active outages.
• SLO rollups: Use Group by and Reduce to compute per‑service error rates, then join with a services table to add owners and on-call rotations to the same view.

For each pattern, pre-aggregate at the source when possible and keep joins narrow (few columns, small row counts).

Troubleshooting: Common Pitfalls and How to Fix Them

• “I can’t select a join key.” The fields don’t share a name. Alias or rename one side first.
• “My table exploded in size.” You created a many-to-many join. Aggregate or dedupe keys before joining.
• “Types don’t match.” Use Convert field type or cast in your query (for example, CAST(id AS TEXT)).
• “Timestamps look wrong.” Align time zones and convert epoch to time. Use the dashboard’s time picker to verify ranges.
• “The panel is slow.” Use Query Inspector to see which query is slow. Add LIMIT/GROUP BY/WHERE clauses; reduce data size early.
• “The map shows nothing.” Check that lat/long fields are numeric and properly named/selected in panel field mappings.
• “Alert didn’t fire as expected.” Alert rules typically don’t apply panel transformations. Compute a single alert-ready metric via the data source or alert expressions designed for alerts.

A Quick Build Checklist

• Define the question: What decision will this panel help someone make?
• Identify join keys and field names across sources.
• Filter and aggregate at the source first.
• Use Mixed data source only when you truly need cross-source results.
• Add queries, label clearly, then apply transformations in a logical order.
• Set units, thresholds, and value mappings for clarity.
• Test with different time ranges and variable selections.
• Inspect performance and refine queries before sharing widely.

Summary

Working with multiple data sources in Grafana is a superpower when you use it thoughtfully. The core ideas are simple:

• Bring in multiple queries per panel and label them clearly.
• Use transformations to merge or join data frames, renaming fields to align keys.
• Filter and aggregate at the source to keep dashboards fast.
• Standardize schemas, handle types and time zones carefully, and document your panels.

With these practices, you can build rich, reliable Grafana dashboards that connect the dots across systems, teams, and data formats—so your organization can move from many sources to one clear picture.