Docs Self-Managed Manage Iceberg Specify Iceberg Schema Specify Iceberg Schema This feature requires an enterprise license. To get a trial license key or extend your trial period, generate a new trial license key. To purchase a license, contact Redpanda Sales. If Redpanda has enterprise features enabled and it cannot find a valid license, restrictions apply. In Iceberg-enabled clusters, the redpanda.iceberg.mode topic property determines how Redpanda maps topic data to the Iceberg table structure. You can have the generated Iceberg table match the structure of a schema in the Schema Registry, or you can use the key_value mode where Redpanda stores the record values as-is in the table. Supported Iceberg modes Redpanda supports the following modes for Iceberg topics: key_value Creates an Iceberg table using a simple schema, consisting of two columns, one for the record metadata including the key, and another binary column for the record’s value. value_schema_id_prefix 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 Schema Registry and producers must write to the topic using the Schema Registry wire format. In the 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 Creates an Iceberg table whose structure matches the latest schema registered for the subject in the Schema Registry. You must register a schema in the Schema Registry. Producers cannot use the wire format in value_schema_latest mode. Redpanda expects the serialized message as-is without the magic byte or schema ID prefix in the record value. The value_schema_latest mode is not compatible with the rpk topic produce command which embeds the wire format header. You must use your own producer code to produce to topics in value_schema_latest mode. The latest schema is cached periodically. The cache period is defined by the cluster property iceberg_latest_schema_cache_ttl_ms (default: 5 minutes). disabled Default for redpanda.iceberg.mode. Disables writing to an Iceberg table for the topic. The following modes are compatible with producing to an Iceberg topic using Redpanda Console: key_value Starting in version 25.2, value_schema_latest with a JSON schema Otherwise, records may fail to write to the Iceberg table and instead write to the dead-letter queue. Configure Iceberg mode for a topic You can set the Iceberg mode for a topic when you create the topic, or you can update the mode for an existing topic. Option 1. Create a new topic and set redpanda.iceberg.mode: rpk topic create <topic-name> --topic-config=redpanda.iceberg.mode=<iceberg-mode> Option 2. Set redpanda.iceberg.mode for an existing topic: rpk topic alter-config <topic-name> --set redpanda.iceberg.mode=<iceberg-mode> Override value_schema_latest default In value_schema_latest mode, you only need to set the property value to the string value_schema_latest. This enables the default behavior of value_schema_latest mode, which determines the subject for the topic using the TopicNameStrategy. For example, if your topic is named sensor the schema is looked up in the sensor-value subject. For Protobuf data, the default behavior also deserializes records using the first message defined in the corresponding Protobuf schema stored in the Schema Registry. If you use a different strategy other than the topic name to derive the subject name, you can override the default behavior of value_schema_latest mode and explicitly set the subject name. To override the default behavior, use the following optional syntax: value_schema_latest:subject=<subject-name>,protobuf_name=<protobuf-message-full-name> For both Avro and Protobuf, specify a different subject name by using the key-value pair subject=<subject-name>, for example value_schema_latest:subject=sensor-data. For Protobuf only: Specify a different message definition by using a key-value pair protobuf_name=<message-full-name>. You must use the fully qualified name, which includes the package name, for example, value_schema_latest:protobuf_name=com.example.manufacturing.SensorData. To specify both a different subject and message definition, separate the key-value pairs with a comma, for example: value_schema_latest:subject=my_protobuf_schema,protobuf_name=com.example.manufacturing.SensorData. If you don’t specify the fully qualified Protobuf message name, Redpanda pauses the data translation to the Iceberg table until you fix the topic misconfiguration. How Iceberg modes translate to table format Redpanda generates an Iceberg table with the same name as the topic. In each mode, Redpanda writes to a redpanda table column that stores a single Iceberg struct per record, containing nested columns of the metadata from each record, including the record key, headers, timestamp, the partition it belongs to, and its offset. For example, if you produce to a topic ClickEvent according to the following Avro schema: { "type": "record", "name": "ClickEvent", "fields": [ { "name": "user_id", "type": "int" }, { "name": "event_type", "type": "string" }, { "name": "ts", "type": "string" } ] } The key_value mode writes to the following table format: CREATE TABLE ClickEvent ( redpanda struct< partition: integer NOT NULL, timestamp: timestamp NOT NULL, offset: long NOT NULL, headers: array<struct<key: binary NOT NULL, value: binary>>, key: binary >, value binary ) Use key_value mode if you want 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: CREATE TABLE ClickEvent ( redpanda struct< partition: integer NOT NULL, timestamp: timestamp NOT NULL, offset: long NOT NULL, headers: array<struct<key: binary NOT NULL, value: binary>>, key: binary >, user_id integer NOT NULL, event_type string, ts string ) As you produce records to the topic, the data also becomes available in object storage for Iceberg-compatible clients to consume. You can use the same analytical tools to read the Iceberg topic data in a data lake as you would for a relational database. If Redpanda fails to translate the record to the columnar format as defined by the schema, it writes the record to a dead-letter queue (DLQ) table. See Troubleshoot errors for more information. Schema types translation Redpanda supports direct translations of the following types to Iceberg value domains: Avro Protobuf JSON Schema Avro type Iceberg type boolean boolean int int long long float float double double bytes binary string string record struct array list map map fixed fixed* decimal decimal uuid uuid* date date time time* timestamp timestamp *These types are not currently supported in Unity Catalog managed Iceberg tables. There are some cases where the Avro type does not map directly to an Iceberg type and Redpanda applies the following transformations: Different flavors of time (such as time-millis) and timestamp (such as timestamp-millis) types are translated to the same Iceberg time and timestamp types, respectively. Avro unions are flattened to Iceberg structs with optional fields. For example: The union ["int", "long", "float"] is represented as an Iceberg struct struct<0 INT NULLABLE, 1 LONG NULLABLE, 2 FLOAT NULLABLE>. The union ["int", null, "float"] is represented as an Iceberg struct struct<0 INT NULLABLE, 1 FLOAT NULLABLE>. All fields are required by default. (Avro always sets a default in binary representation.) Some Avro types are not supported: The Avro duration logical type is ignored. The Avro null type is ignored and not represented in the Iceberg schema. Recursive types are not supported. Protobuf type Iceberg type bool boolean double double float float int32 int sint32 int int64 long sint64 long sfixed32 int sfixed64 long string string bytes binary map map message struct There are some cases where the Protobuf type does not map directly to an Iceberg type and Redpanda applies the following transformations: Repeated values are translated into Iceberg list types. Enums are translated into Iceberg int types based on the integer value of the enumerated type. uint32 and fixed32 are translated into Iceberg long types as that is the existing semantic for unsigned 32-bit values in Iceberg. uint64 and fixed64 values are translated into their Base-10 string representation. google.protobuf.Timestamp is translated into timestamp in Iceberg. Recursive types are not supported. Requirements: Only JSON Schema Draft-07 is currently supported. You must declare the JSON Schema dialect using the $schema keyword, for example "$schema": "http://json-schema.org/draft-07/schema#". You must use a JSON Schema that constrains JSON documents to a strict type in order for Redpanda to translate to Iceberg; that is, each subschema must use the type keyword. Valid JSON Schema example { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "productId": { "type": "integer" }, "tags": { "type": "array", "items": { "type": "string" } } } } JSON type Iceberg type Notes array list The keywords items and additionalItems must be used to constrain element types. boolean boolean null The null type is not supported except when it is paired with another type to indicate nullability. number double integer long string string The format keyword can be used for custom Iceberg types. See format annotation translation for details. object struct The properties keyword must be used to define struct fields and constrain their types. The additionalProperties keyword is accepted only when it is set to false. Table 1. format annotation translation format value Iceberg type date-time timestamptz date date time time The following are not supported for JSON Schema: Relative and absolute (including external) references using $ref and $dynamicRef keywords The default keyword Conditional typing (if, then, else, dependent keywords) Boolean JSON Schema combinations (allOf, anyOf, oneOf keywords) Dynamic object members (patternProperties and additionalProperties (except when it is set to false) keywords) See also redpanda.iceberg.mode topic property reference Suggested labs Redpanda Iceberg Docker Compose ExampleIceberg Streaming on Kubernetes with Redpanda, MinIO, and SparkSearch all labs Back to top × Simple online edits For simple changes, such as fixing a typo, you can edit the content directly on GitHub. Edit on GitHub Or, open an issue to let us know about something that you want us to change. Open an issue Contribution guide For extensive content updates, or if you prefer to work locally, read our contribution guide . Was this helpful? thumb_up thumb_down group Ask in the community mail Share your feedback group_add Make a contribution 🎉 Thanks for your feedback! About Iceberg Topics Use Iceberg Catalogs