Create an Iceberg table with an explicit partition spec using Spark and the Iceberg Spark runtime

Steps

1. Add the Iceberg Spark runtime JAR to your Spark session and configure a catalog (e.g., spark.sql.catalog.my_catalog = org.apache.iceberg.spark.SparkCatalog) along with catalog properties pointing to your chosen catalog type.
2. Create the table with CREATE TABLE my_catalog.db.events (id BIGINT, event_time TIMESTAMP, region STRING, payload STRING) USING iceberg in Spark SQL.
3. Define a partition spec with PARTITIONED BY (days(event_time), region) to apply a day transform on the timestamp column alongside an identity partition on region.
4. Insert data with INSERT INTO my_catalog.db.events VALUES (...) and verify partitions are created as expected by querying the partitions metadata table: SELECT * FROM my_catalog.db.events.partitions.
5. Optionally alter the partition spec later with ALTER TABLE my_catalog.db.events ADD PARTITION FIELD bucket(16, id) to add a bucket transform without rewriting existing data.