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@@ -28,20 +28,18 @@ The second step is deciding how you want to aggregate the grouped data. When
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using aggregations, you practically ask questions about the index. There are
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different types of aggregations, each with its own purpose and output. To learn
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more about the supported aggregations and group-by fields, see
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-{ref}/transform-resource.html[{transform-cap} resources].
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+<<put-transform>>.
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As an optional step, you can also add a query to further limit the scope of the
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aggregation.
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The {transform} performs a composite aggregation that paginates through all the
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data defined by the source index query. The output of the aggregation is stored
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-in a destination index. Each time the {transform} queries the source index, it
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+in a _destination index_. Each time the {transform} queries the source index, it
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creates a _checkpoint_. You can decide whether you want the {transform} to run
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-once (batch {transform}) or continuously ({ctransform}). A batch {transform} is a
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-single operation that has a single checkpoint. {ctransforms-cap} continually
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-increment and process checkpoints as new source data is ingested.
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-
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-.Example
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+once or continuously. A _batch {transform}_ is a single operation that has a
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+single checkpoint. _{ctransforms-cap}_ continually increment and process
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+checkpoints as new source data is ingested.
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Imagine that you run a webshop that sells clothes. Every order creates a
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document that contains a unique order ID, the name and the category of the
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@@ -72,7 +70,7 @@ indices then index the results into the destination index. Therefore, a
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aggregation that it performs and the indexing process.
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For better performance, make sure that your search aggregations and queries are
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-optimized, so they don't process unnecessary data.
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+optimized and that your {transform} is processing only necessary data.
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NOTE: When you use <<search-aggregations-bucket-datehistogram-aggregation>>, the
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queries are not considered optimal as they run through a significant amount of
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Lisa Cawley