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Copy file name to clipboardExpand all lines: concepts/data-pipeline/filter.md
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description: Modify, Enrich or Drop your records
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description: Modify, enrich or drop your records
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# Filter
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In production environments we want to have full control of the data we are collecting, filtering is an important feature that allows us to **alter** the data before delivering it to some destination.
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In production environments you want to have full control of the data being collected.
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Filtering lets you alter the collected data before delivering it to a destination.
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```mermaid
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graph LR
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style C stroke:darkred,stroke-width:2px;
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```
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Filtering is implemented through plugins, so each filter available could be used to match, exclude or enrich your logs with some specific metadata.
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Filtering is implemented through plugins. Each available filter could be used to
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match, exclude or enrich your logs with specific metadata.
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We support many filters, A common use case for filtering is Kubernetes deployments. Every Pod log needs to get the proper metadata associated
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Fluent Bit support many filters. A common use case for filtering is Kubernetes
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deployments. Every pod log needs the proper metadata associated.
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Very similar to the input plugins, Filters run in an instance context, which has its own independent configuration. Configuration keys are often called **properties**.
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Like input plugins, filters run in an instance context, which has its own independent
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configuration. Configuration keys are often called _properties_.
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For more details about the Filters available and their usage, please refer to the [Filters](https://docs.fluentbit.io/manual/pipeline/filters) section.
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For more details about the Filters available and their usage, see
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