Begin by exporting your data from Pocket. Pocket allows you to export your saved items in HTML or JSON format directly from your account settings. Navigate to your Pocket account settings, look for the data export option, and download your data in the preferred format, ideally JSON for easier parsing and manipulation.

Once you have the JSON file from Pocket, parse this file using a programming language like Python. Python has excellent libraries such as `json` to read and manipulate JSON data. This step involves reading the JSON file and transforming it into a structured format like a list of dictionaries, where each dictionary represents a Pocket item.

Apache Iceberg requires data to be in a structured format such as Parquet or ORC. Use a data processing library like Pandas to transform the JSON data into a tabular format. Convert the structured data into a DataFrame and ensure that the data types are consistent and suitable for conversion into a columnar storage format.

Use the same data processing tool to convert the DataFrame into a Parquet file. For example, with Pandas, you can use `to_parquet()` to save the DataFrame as a Parquet file. This step is crucial as Apache Iceberg works efficiently with Parquet files due to their columnar storage capabilities.

Ensure that your environment is set up with Apache Iceberg. This involves installing Apache Iceberg and its dependencies, such as Apache Spark or Flink, depending on your processing needs. Configure Iceberg to work with your file system or storage solution, such as HDFS or S3.

Use Apache Spark or Flink to create an Iceberg table and load the Parquet file into it. With Spark, you can use the Iceberg Spark extension to create a table and write data into it using Spark SQL. For example, in Spark, you might use: ```sql
CREATE TABLE my_table (id STRING, title STRING, url STRING) USING iceberg;
```
Followed by:
```python
spark.read.parquet('path_to_parquet_file').writeTo('my_table').append()
```

Finally, ensure the data has been accurately imported into your Iceberg table. Perform some basic queries to verify the data's integrity and consistency. Use Spark SQL or another compatible query engine to run simple select queries and check that the data matches your expectations from the original Pocket data.
By following these steps, you can manually move data from Pocket to Apache Iceberg effectively, without relying on third-party connectors or integrations.