Begin by exporting the data from ClickUp. Log into your ClickUp account and navigate to the workspace or project you wish to export. In the settings, find the export option. ClickUp typically allows data export in CSV or JSON format. Choose your preferred format and download the file to your local system.

Ensure your local machine has the necessary tools installed for handling data manipulation. Install Python and any necessary libraries such as pandas for data manipulation, and pyarrow for Apache Arrow support, which is useful when dealing with Parquet files, a format Iceberg commonly uses.

Load the exported ClickUp data into a Python environment using pandas. This allows you to clean and transform the data according to your requirements. Use pandas to read the CSV/JSON file, perform any necessary data cleaning, and transform the data structure to match the schema you intend to use in Apache Iceberg.

Once the data is cleaned and transformed, convert it into Parquet format. Apache Iceberg works efficiently with Parquet files. Use pandas and pyarrow to write the DataFrame to a Parquet file. Here's a basic example in Python:

```python
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

df = pd.read_csv('clickup_data.csv') # or pd.read_json('clickup_data.json')
table = pa.Table.from_pandas(df)
pq.write_table(table, 'clickup_data.parquet')
```

Ensure that your Apache Iceberg environment is properly set up. Install Apache Iceberg in a compatible environment, such as Apache Hive, Spark, or Flink. Configure your environment by setting up the necessary catalog and schema where you will store your data.

Move the Parquet file to a location accessible by your Iceberg-compatible environment. Use the query engine (like Spark SQL or Hive) to load the Parquet file into a new or existing Iceberg table. For example, using Spark:

```python
from pyspark.sql import SparkSession

spark = SparkSession.builder \
.appName("IcebergDataLoad") \
.config("spark.sql.catalog.my_catalog", "org.apache.iceberg.spark.SparkCatalog") \
.config("spark.sql.catalog.my_catalog.type", "hadoop") \
.config("spark.sql.catalog.my_catalog.warehouse", "path/to/warehouse") \
.getOrCreate()

df = spark.read.parquet("path/to/clickup_data.parquet")
df.writeTo("my_catalog.my_database.my_table").createOrReplace()
```

After loading the data, verify its integrity and correctness. Use SQL queries to check the data within the Iceberg table, ensuring it matches the original ClickUp export. Verify the schema, data types, and row counts to confirm the data has been transferred accurately.

By following these steps, you can successfully move data from ClickUp to Apache Iceberg without the need for third-party connectors or integrations.