Data pipeline: condense filters

Signed-off-by: Lynette Miles <[email protected]>

Author: esmerel

concepts/data-pipeline.md

@@ -2,6 +2,13 @@
 
 The Fluent Bit data pipeline incorporates several specific concepts.
 
-## Buffering
+## Buffer
 
 The [`buffer`](./buffering.md) phase in the pipeline aims to provide a unified and persistent mechanism to store your data, using the primary in-memory model or the file system-based mode.
+
+## Filter
+
+In production environments you need full control of the data you're collecting. Filtering lets you alter the collected data before delivering it to a destination.
+For more details about the Filters available and their usage, see [Filters](https://docs.fluentbit.io/manual/pipeline/filters).
+
+## Inputs

concepts/data-pipeline/filter.md renamed to pipeline/filters.md

@@ -1,8 +1,4 @@
----
-description: Modify, enrich or drop your records
----
-
-# Filter
+# Filters
 
 In production environments you need full control of the data you're collecting. Filtering lets you alter the collected data before delivering it to a destination.
 
@@ -25,5 +21,3 @@ Filtering is implemented through plugins. Each available filter can be used to m
 Fluent Bit support many filters. A common use case for filtering is Kubernetes deployments. Every pod log needs the proper metadata associated with it.
 
 Like input plugins, filters run in an instance context, which has its own independent configuration. Configuration keys are often called _properties_.
-
-For more details about the Filters available and their usage, see [Filters](https://docs.fluentbit.io/manual/pipeline/filters).