1. Install the Google Cloud SDK if you haven't already, and authenticate to your GCP account using `gcloud auth login`.
2. Install Apache Hadoop and Apache Spark. Ensure that they are properly configured to work together.
3. Add Apache Iceberg libraries to your Spark environment. You can do this by including the Iceberg dependency in your build file or by submitting Spark jobs with the Iceberg JARs.

Use the Google Cloud SDK to download the data from your GCS bucket to your local machine or server. You can use the `gsutil cp` command to copy files from GCS. For example:

gsutil cp gs://your-bucket-name/path/to/data/* /local/path/to/store/data/

1. Add Iceberg tables to your Hive catalog or use Hadoop Tables if you prefer not to use Hive.
2. Initialize the Iceberg table using Spark or directly through the Iceberg API. For example, using Spark:

  val spark = SparkSession.builder()
    .appName("Iceberg")
    .config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
    .config("spark.sql.catalog.local.type", "hadoop")
    .config("spark.sql.catalog.local.warehouse", "hdfs:///path/to/iceberg/warehouse")
    .getOrCreate()

  spark.sql("CREATE TABLE local.db.table_name (id bigint, data string) USING iceberg")

1. Using Spark, read the data that was downloaded from GCS into a DataFrame. For example:

val df = spark.read.format("json").load("/local/path/to/store/data/") // Use the appropriate format for your data

2. Write the DataFrame to the Iceberg table:

df.write.format("iceberg").mode("append").save("local.db.table_name")

Once the data has been written to the Iceberg table, you can perform a read operation to verify that the transfer was successful:

val results = spark.read.format("iceberg").load("local.db.table_name")
results.show()

After verifying the data transfer, you can remove the local copies of the data that were downloaded from GCS to free up space.

1. The above steps are suitable for a one-time transfer or proof of concept. For production use, you may want to automate the process using Apache Airflow, Apache NiFi, or another workflow management tool.
2. Consider implementing monitoring, logging, and alerting to keep track of the data transfer process and quickly identify any issues.
3. Ensure that your data transfer adheres to your organization's data governance and compliance policies.