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Author: kbatuigas

modules/manage/pages/iceberg/choose-iceberg-mode.adoc

@@ -33,7 +33,7 @@ Creates an Iceberg table using a simple schema, consisting of two columns, one f
 
 Creates an Iceberg table whose structure matches the Redpanda schema for the topic, with columns corresponding to each field. You must register a schema in the xref:manage:schema-reg/schema-reg-overview.adoc[Schema Registry] and producers must write to the topic using the Schema Registry wire format.
 
-In the xref:manage:schema-reg/schema-reg-overview.adoc#serialization-and-deserialization[Schema Registry wire format], a "magic byte" and schema ID are embedded in the message payload header. Producers to the topic must use the wire format in the serialization process so Redpanda can determine the schema used for each record, use the schema to define the Iceberg table, and store the topic values in the corresponding table columns.
+In the xref:manage:schema-reg/schema-reg-overview.adoc#wire-format[Schema Registry wire format], a "magic byte" and schema ID are embedded in the message payload header. Producers to the topic must use the wire format in the serialization process so Redpanda can determine the schema used for each record, use the schema to define the Iceberg table, and store the topic values in the corresponding table columns.
 
 === value_schema_latest
 
@@ -130,7 +130,7 @@ CREATE TABLE ClickEvent (
 )
 ----
 
-Use `key_value` mode if the topic data is in JSON or if you can use the Iceberg data in its semi-structured format.
+Use `key_value` mode if the topic data is in JSON or if you are able to use the Iceberg data in its semi-structured format.
 
 The `value_schema_id_prefix` and `value_schema_latest` modes can use the schema to translate to the following table format:
 

modules/manage/pages/schema-reg/schema-reg-overview.adoc

@@ -4,7 +4,7 @@
 // tag::single-source[]
 :description: Redpanda's Schema Registry provides the interface to store and manage event schemas.
 
-In Redpanda, the messages exchanged between producers and consumers contain raw bytes. Schemas enable producers and consumers to share the information needed to <<serialization-and-deserialization,serialize and deserialize>> those messages. They register and retrieve the schemas they use in the Schema Registry to ensure data verification.
+In Redpanda, the messages exchanged between producers and consumers contain raw bytes. Schemas enable producers and consumers to share the information needed to serialize and deserialize those messages. They register and retrieve the schemas they use in the Schema Registry to ensure data verification.
 
 Schemas are versioned, and the registry supports configurable compatibility modes between schema versions. When a producer or a consumer requests to register a schema change, the registry checks for schema compatibility and returns an error for an incompatible change. Compatibility modes can ensure that data flowing through a system is well-structured and easily evolves. 
 

modules/manage/partials/iceberg/query-iceberg-topics.adoc

@@ -76,7 +76,7 @@ rpk registry schema create ClickEvent-value --schema path/to/schema.avsc --type
 echo '"key1" {"user_id":2324,"event_type":"BUTTON_CLICK","ts":"2024-11-25T20:23:59.380Z"}' | rpk topic produce ClickEvent --format='%k %v\n' --schema-id=topic
 ----
 +
-The `value_schema_id_prefix` mode requires that you produce to a topic using the xref:manage:schema-reg/schema-reg-overview.adoc#serialization-and-deserialization[Schema Registry wire format], which includes the magic byte and schema ID in the prefix of the message payload. This allows Redpanda to identify the correct schema version in the Schema Registry for a record. 
+The `value_schema_id_prefix` mode requires that you produce to a topic using the xref:manage:schema-reg/schema-reg-overview.adoc#wire-format[Schema Registry wire format], which includes the magic byte and schema ID in the prefix of the message payload. This allows Redpanda to identify the correct schema version in the Schema Registry for a record. 
 
 . The following Spark SQL query returns values from columns in the `ClickEvent` table, with the table structure derived from the schema, and column names matching the schema fields. If you've integrated a catalog, query engines such as Spark SQL provide Iceberg integrations that allow easy discovery and access to existing Iceberg tables in object storage.
 +