> For the complete documentation index, see [llms.txt](https://docs.enlyze.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.enlyze.com/en/integrations/grafana/intermediate-features/02-transformations.md). # 02 Transformations Transformations process data after the query but before visualization: aggregate, filter, rename, combine. In this tutorial you will learn the most important transformations and build a summary table like this step by step:
## What you will learn * Aggregate data with Reduce (minimum, maximum, mean) * Rename, reorder, and hide fields * Add trend lines with regression analysis * Combine data from multiple queries * Chain transformations ## Prerequisites * Completed Part 1 and Part 2 of the tutorials * Basic knowledge of panel creation *** ## The Transform tab 1. Edit a panel 2. Click the **Transform** tab (between Query and Alert) 3. Click **+ Add transformation** and select a transformation Transformations are executed from top to bottom. You can reorder them via drag and drop, disable them (eye icon), or delete them (trash icon). {% hint style="info" %} The order affects the result. A filter before Reduce produces different results than after. {% endhint %} *** ## Reduce Converts time series data into aggregated values. Ideal for summary tables. 1. Add the **Reduce** transformation 2. Select the calculations: | Calculation | Description | | ---------------------- | ------------------------------------ | | **Last \*** | Last non-null value | | **Min** / **Max** | Minimum / Maximum | | **Mean** | Average | | **Sum** | Sum of all values | | **Difference percent** | Percentage change | | **All values** | Array of all values (for sparklines) |
*** ## Organize fields by name Rename, reorder, and hide columns. Essential for clean tables. 1. Add the **Organize fields by name** transformation 2. For each field: * **Rename**: Enter a new display name * **Hide**: Click the eye icon to hide * **Reorder**: Drag and drop to reorder
*** ## Rename by regex Rename multiple fields at once using pattern matching. | Setting | Description | | ----------- | -------------------------------------------- | | **Match** | Regex pattern to match | | **Replace** | Replacement text ($1, $2 for capture groups) | **Examples:** | Match | Replace | Before | After | | ------------------------ | ---------- | -------------------- | -------------- | | `Extruder (.) Durchsatz` | `Ext $1` | Extruder A Durchsatz | Ext A | | `\(Soll\)` | `(Target)` | Speed (Soll) | Speed (Target) | | `(.+) - (.+)` | `$2` | Kiefel - Durchsatz | Durchsatz |
### Common regex patterns | Pattern | Description | Example | | ----------- | ----------------------------------------- | ----------------------------------- | | `.` | Any single character | `A`, `1`, `-` | | `.*` | Any number of characters | `Extruder A Durchsatz` | | `.+` | At least one character | like `.*`, but not empty | | `(...)` | Capture group (use `$1`, `$2` in Replace) | `(Extruder .)` → `$1` | | `\(` / `\)` | Literal parentheses | `\(Soll\)` matches `(Soll)` | | `^` / `$` | Start / end of string | `^Kiefel` matches only at the start | | `[A-C]` | Character class | `Extruder [A-C]` | | `\d+` | One or more digits | `Sensor \d+` matches `Sensor 42` | *** ## Filter by name Show only specific fields in the output. 1. Add the **Filter by name** transformation 2. Select **Include** (show only selected fields) or **Exclude** (hide selected fields) 3. Select fields manually via checkboxes or filter by regex
**Regex examples for Filter by name:** | Mode | Regex | Effect | | ------- | ----------------------- | ------------------------------------------- | | Include | `Durchsatz\|Temperatur` | Show only throughput and temperature fields | | Exclude | `.*\(Soll\)` | Hide all setpoint values | | Include | `Extruder [AB].*` | Show only Extruder A and B | {% hint style="info" %} With **Filter data by values** you can also filter rows by conditions, for example only showing data points where the temperature is above 200 °C. {% endhint %} *** ## Regression analysis Add trend lines to identify how data evolves over time. 1. Add the **Regression analysis** transformation 2. Set **X field** to the time field and **Y field** to the value field
The regression line shows the overall trend: rising, falling, or stable.
### Style the trend line By default, the regression line has no unit and appears on a separate Y-axis. Add an override: 1. Match the field by regex: `.*predicted.*` 2. Set the **Unit** to the same unit as the original data 3. Choose a different color (for example red) and a dashed line style *** ## Combine data When you want to merge data from multiple queries, two transformations are available: | Transformation | Description | | ----------------- | -------------------------------------------------------------------------------------------------- | | **Merge** | Combines all time series into one table. No configuration needed. | | **Join by field** | Joins tables on a shared field (for example a timestamp). Mode: Inner, Left outer, or Right outer. | *** ## Example: machine comparison This example shows how to use a transformation chain to summarize the throughput of two machines in a compact comparison table. ### Step 1: Create two queries Create two separate queries, one for each machine. Name the queries after the machine (for example "Kiefel" and "Macchi") so the data can be distinguished later.
### Step 2: Build the transformation chain Switch to the **Transform** tab and add the following five transformations one after another:
1. **Rename by regex** (x2): Rename the "Durchsatz" field in each query so the machine is identifiable. Match: `Durchsatz`, Replace: `Kiefel` or `Macchi`. Click the filter icon on the transformation and under **Apply transformation to** select the corresponding query (for example "Query: Kiefel"). This way each rename only applies to its own query. 2. **Join by field**: Joins the two renamed time series on the timestamp into a single table. 3. **Reduce**: Aggregates the time series into one row per machine with the calculations Last \*, Min, Max, Mean, All values, and Difference. 4. **Organize fields by name**: Renames the columns to be more readable (for example "Field" to "Machine", "Last \*" to "Current", "All values" to "Trend") and sorts them into the desired order. ### Result The finished table compares both machines at a glance, including a sparkline trend display:
The column formatting (Bar Gauge for "Current", colored background for "Change", Sparkline for "Trend") is configured via **Overrides** > **Fields with name** > **Cell options**. For details, see the tutorial [Tables](/en/integrations/grafana/visualisation-types/03-tables.md). {% hint style="info" %} Use the **Table view** toggle in the panel editor to check intermediate results after each transformation. {% endhint %} *** ## Tips * **Performance**: Transformations run in the browser. Filter at the query level when possible. * **Debugging**: Add transformations one at a time and use the **Table view** toggle to check intermediate results. * **Common patterns**: Time series to summary table: Reduce + Organize fields. Sparklines: Reduce with All values + Override on cell type. *** ## Next steps * [**Introduction to the ENLYZE API**](/en/integrations/grafana/advanced-api/01-introduction.md) - API-based dashboards for advanced use cases --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.enlyze.com/en/integrations/grafana/intermediate-features/02-transformations.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.