Begin by exporting your data from Webflow. Navigate to the Webflow dashboard, select the project containing the data, and look for the CMS Collections tab. Export the desired collections as CSV files. Webflow provides a built-in option to export collection data, which can be downloaded directly to your local system.

Set up your local environment to work with Apache Iceberg. Ensure you have Apache Spark or Flink installed, as Iceberg is commonly used with these engines. You will also need Java and a compatible build tool like Maven or Gradle to compile and run your Iceberg setup.

Download and install Apache Iceberg. You can do this by cloning the Iceberg repository from GitHub or downloading a pre-built package. Follow the installation instructions specific to your processing engine (e.g., Spark or Flink) as outlined in the Iceberg documentation. This will typically involve adding Iceberg to your classpath and configuring it within your data processing engine.

Define the schema for your Iceberg table based on the structure of the CSV data exported from Webflow. This involves specifying the columns and data types that match the exported CSV files. You can do this by writing a DDL (Data Definition Language) script or using a Spark/Flink job to define the schema programmatically.

Write a script to load the CSV data into your chosen processing engine. Use Spark's `spark.read.csv()` or Flink's `CsvInputFormat` to read the CSV files. Ensure that the data is loaded into a DataFrame or DataSet, conforming to the schema defined in the previous step.

Use your data processing engine to write the data into the Iceberg table. In Spark, this can be done using the `write` method on the DataFrame, like `dataframe.write.format("iceberg").save("table_name")`. In Flink, configure a sink that writes to Iceberg. Ensure that the data types and structure match the Iceberg table schema to avoid errors.

After the data transfer, verify that the data in the Iceberg table matches the original CSV data. You can run queries to check for consistency and completeness. Perform any necessary maintenance tasks, such as optimizing the data layout within Iceberg using compaction or cleaning up temporary files, to ensure efficient storage and query performance.

By completing these steps, you will have successfully moved data from Webflow to Apache Iceberg without relying on third-party connectors. This manual process leverages the built-in capabilities of Webflow, Apache Iceberg, and your chosen data processing engine.