add more info

Author: 317brian

docs/ingestion/data-formats.md

@@ -752,8 +752,8 @@ FROM "wikiticker"
 This query returns:
 
 | `kafka.header.env` | `kafka.key` | `kafka.partition` | `kafka.offset` | `kafka.timestamp` | `kafka.topic` |
-|--------------------|-------------|-------------------|----------------|-------------------|---------------|
-| `development`      | `wiki-edit` | `0`               | `12345`        | `1680795276351`   | `wiki-edits`  |
+|--------------------|-----------|-------------------|----------------|---------------|---------------|
+| `development`      |`wiki-edit`|`0`|`12345`| `1680795276351`| `wiki-edits`  |
 
 ### Kinesis
 

docs/ingestion/ingestion-spec.md

@@ -188,9 +188,13 @@ Treat `__time` as a millisecond timestamp: the number of milliseconds since Jan
 The `dimensionsSpec` is located in `dataSchema` → `dimensionsSpec` and is responsible for
 configuring [dimensions](./schema-model.md#dimensions).
 
-You can either manually specify the dimensions or take advantage of schema auto-discovery where you allow Druid to infer all or some of the schema for your data. This means that you don't have to explicitly specify your dimensions and their type. 
+You can either manually specify the dimensions or take advantage of type-aware schema auto-discovery where you allow Druid to infer all or some of the schema for your data. This means that you don't have to explicitly specify your dimensions and their type. 
 
-To use schema auto-discovery, set `useSchemaDiscovery` to `true`. 
+:::caution
+When using type-aware schema auto-discovery, Druid discovers the type for all dimensions unless you explicitly specify dimensions for Druid to discover or list dimensions in the exclusion list. This helps you control storage costs by preventing Druid from ingesting dimensions unintentionally.
+:::
+
+To use type-aware schema auto-discovery, set `useSchemaDiscovery` to `true`. 
 
 Alternatively, you can use the string-based schemaless ingestion where any discovered dimensions are treated as strings. To do so, leave `useSchemaDiscovery` set to `false` (default). Then, set the dimensions list to empty or set the  `includeAllDimensions` property to `true`.
 

docs/ingestion/schema-design.md

@@ -254,7 +254,7 @@ You can have Druid infer the schema and types for your data partially or fully b
 Before you use type-aware schema discovery, keep the following in mind:
 
 - There maybe impact on downstream BI tools depending on how they handle ARRAY-typed columns.
-- You may ingest more columns than you intend if you don't maintain an exclusion list. For example, if you use type-aware schema discovery and the Kafka input format, you'll ingest optional dimensions like the Kafka offset and partition unless you add them to the exclusion list.
+- You may ingest more columns than you intend if you don't maintain an exclusion list. For example, if you use type-aware schema discovery and the Kafka input format, you'll ingest optional dimensions like the Kafka offset and partition unless you add them to the exclusion list or explicitly list the columns you want Druid to discover the type for.
 
 When performing type-aware schema discovery, Druid can discover all the columns of your input data (that are not present in
 the exclusion list). Druid automatically chooses the most appropriate native Druid type among `STRING`, `LONG`,