Begin by reviewing the documentation or support resources of Orbit Love to determine how to export data. Orbit Love might offer options like CSV, JSON, or direct API access. Familiarize yourself with these export formats as they will dictate how you can extract data.

Use the identified method to export data. If Orbit Love provides an API, write a script using a programming language like Python to call the API and save the data locally. If a direct export option like CSV or JSON is available, use it to manually export the data files.

Once the data is exported, examine its structure and format. Clean and preprocess the data as necessary to ensure it is in a consistent format suitable for loading into Apache Iceberg. This may involve renaming fields, changing data types, or normalizing values.

If you haven't already, set up an Apache Iceberg environment. This involves configuring Apache Hive, Presto, or Apache Spark with Iceberg support. Ensure your environment is properly configured to create and manage Iceberg tables.

Write a transformation script using Apache Spark or another supported tool to convert your preprocessed data into a format compatible with Apache Iceberg. Typically, this involves converting your data into Parquet files, as Iceberg natively supports this format.

Use Apache Spark to load the transformed data into an Iceberg table. Define the schema and partitioning strategy as needed. This can be done through a Spark DataFrame API like `spark.write.format("iceberg").save("path/to/iceberg/table")`.

Once the data is loaded into Iceberg, perform checks to ensure data integrity and completeness. Query the Iceberg table to verify the data looks correct and matches the original export from Orbit Love. This step ensures that no data was lost or corrupted during the transfer process.

By following these steps, you can systematically transfer data from Orbit Love to Apache Iceberg, leveraging native capabilities without relying on third-party connectors or integrations.