Data collection transformations in Azure Monitor
With transformations in Azure Monitor, you can filter or modify incoming data before it's sent to a Log Analytics workspace. This article provides a basic description of transformations and how they're implemented. It provides links to other content for creating a transformation.
Transformations are performed in Azure Monitor in the data ingestion pipeline after the data source delivers the data and before it's sent to the destination. The data source might perform its own filtering before sending data but then rely on the transformation for further manipulation before it's sent to the destination.
Transformations are defined in a data collection rule (DCR) and use a Kusto Query Language (KQL) statement that's applied individually to each entry in the incoming data. It must understand the format of the incoming data and create output in the structure expected by the destination.
The following diagram illustrates the transformation process for incoming data and shows a sample query that might be used. See Structure of transformation in Azure Monitor for details on building transformation queries.
Why to use transformations
The following table describes the different goals that you can achieve by using transformations.
| Category | Details |
|---|---|
| Remove sensitive data | You might have a data source that sends information you don't want stored for privacy or compliancy reasons. Filter sensitive information. Filter out entire rows or particular columns that contain sensitive information. Obfuscate sensitive information. Replace information such as digits in an IP address or telephone number with a common character. Send to an alternate table. Send sensitive records to an alternate table with different role-based access control configuration. |
| Enrich data with more or calculated information | Use a transformation to add information to data that provides business context or simplifies querying the data later. Add a column with more information. For example, you might add a column identifying whether an IP address in another column is internal or external. Add business-specific information. For example, you might add a column indicating a company division based on location information in other columns. |
| Reduce data costs | Because you're charged ingestion cost for any data sent to a Log Analytics workspace, you want to filter out any data that you don't require to reduce your costs. Remove entire rows. For example, you might have a diagnostic setting to collect resource logs from a particular resource but not require all the log entries that it generates. Create a transformation that filters out records that match a certain criteria. Remove a column from each row. For example, your data might include columns with data that's redundant or has minimal value. Create a transformation that filters out columns that aren't required. Parse important data from a column. You might have a table with valuable data buried in a particular column. Use a transformation to parse the valuable data into a new column and remove the original. Send certain rows to basic logs. Send rows in your data that require basic query capabilities to basic logs tables for a lower ingestion cost. |
| Format data for destination | You might have a data source that sends data in a format that doesn't match the structure of the destination table. Use a transformation to reformat the data to the required schema. |
Supported tables
See Tables that support transformations in Azure Monitor Logs for a list of the tables that can be used with transformations. You can also use the Azure Monitor data reference which lists the attributes for each table, including whether it supports transformations. In addition to these tables, any custom tables (suffix of _CL) are also supported.
- Any Azure table listed in Tables that support transformations in Azure Monitor Logs. You can also use the Azure Monitor data reference which lists the attributes for each table, including whether it supports transformations.
- Any custom table created for the Azure Monitor Agent. (MMA custom table can't use transformations)
Create a transformation
There are multiple methods to create transformations depending on the data collection method. The following table lists guidance for different methods for creating transformations.
| Data collection | Reference |
|---|---|
| Logs ingestion API | Send data to Azure Monitor Logs by using REST API (Azure portal) Send data to Azure Monitor Logs by using REST API (Azure Resource Manager templates) |
| Virtual machine with Azure Monitor agent | Add transformation to Azure Monitor Log |
| Kubernetes cluster with Container insights | Data transformations in Container insights |
Transformation performance
Transformation execution time contributes to overall data ingestion latency. Optimal transformations should take no more than 1 second to run. If a transformation takes more than 20 seconds, you may experience data loss.
Cost for transformations
While transformations themselves don't incur direct costs, the following scenarios can result in additional charges:
- If a transformation increases the size of the incoming data, such as by adding a calculated column, you'll be charged the standard ingestion rate for the extra data.
- If a transformation reduces the ingested data by more than 50%, you'll be charged for the amount of filtered data above 50%.
To calculate the data processing charge resulting from transformations, use the following formula:
[GB filtered out by transformations] - ([GB data ingested by pipeline] / 2). The following table shows examples.
| Data ingested by pipeline | Data dropped by transformation | Data ingested by Log Analytics workspace | Data processing charge | Ingestion charge |
|---|---|---|---|---|
| 20 GB | 12 GB | 8 GB | 2 GB 1 | 8 GB |
| 20 GB | 8 GB | 12 GB | 0 GB | 12 GB |
1 This charge excludes the charge for data ingested by Log Analytics workspace.
To avoid this charge, you should filter ingested data using alternative methods before applying transformations. By doing so, you can reduce the amount of data processed by transformations and, therefore, minimize any additional costs.
See Azure Monitor pricing for current charges for ingestion and retention of log data in Azure Monitor.
Important
If Azure Sentinel is enabled for the Log Analytics workspace, there's no filtering ingestion charge regardless of how much data the transformation filters.