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Ingestion-time data transformation provides customers with more control over the ingested data. Supplementing the pre-configured, hardcoded workflows that create standardized tables, ingestion time-transformation adds the capability to filter and enrich the output tables, even before running any queries. Custom log ingestion uses the Custom Log API to normalize custom-format logs so they can be ingested into certain standard tables, or alternatively, to create customized output tables with user-defined schemas for ingesting these custom logs.
These two mechanisms are configured using Data Collection Rules (DCRs), either in the Log Analytics portal, or via API or ARM template. This article will help you choose which kind of DCR you need for your particular data connector, and direct you to the instructions for each scenario.
Prerequisites
Before you start configuring DCRs for data transformation:
Learn more about data transformation and DCRs in Azure Monitor and Microsoft Sentinel. For more information, see:
Verify data connector support. Make sure that your data connectors are supported for data transformation.
In our data connector reference article, check the section for your data connector to understand which types of DCRs are supported. Continue in this article to understand how the DCR type you select affects the rest of the ingestion and transformation process.
When you're done, come back to Microsoft Sentinel to verify that your data is being ingested based on your newly configured transformation. It may take up to 60 minutes for the data transformation configurations to apply.
Migrate to ingestion-time data transformation
If you currently have custom Microsoft Sentinel data connectors, or built-in, API-based data connectors, you may want to migrate to using ingestion-time data transformation.
Use one of the following methods:
Configure a DCR to define, from scratch, the custom ingestion from your data source to a new table. You might use this option if you want to use a new schema that doesn't have the current column suffixes, and doesn't require query-time KQL functions to standardize your data.
After you've verified that your data is properly ingested to the new table, you can delete the legacy table, as well as your legacy, custom data connector.
Демонстрація розуміння поширених завдань з проектування даних для реалізації та керування навантаженнями на проектування даних у Microsoft Azure за допомогою низки служб Azure.
Learn about how Azure Monitor's custom log ingestion and data transformation features can help you get any data into Microsoft Sentinel and shape it the way you want.
Learn how to configure data ingestion into Microsoft Sentinel from specific or custom applications that produce logs as text files, using the Custom Logs via AMA data connector or manual configuration.