Begin by exporting your data from Airtable. Open your Airtable base, navigate to the table you wish to export, and click on "View" options. Select "Download CSV" to export your table data as a CSV file. This format is widely supported and can be easily manipulated for further processing.

Prepare a local environment to process your data. This involves installing necessary tools such as Python, Java, and Apache Iceberg dependencies. Ensure you have Python and Java installed on your machine. Use a package manager like pip to install any additional libraries you might need, such as `pandas` for manipulating CSV data.

Use Python to read and transform your CSV data into a format suitable for Apache Iceberg. Utilize pandas to load the CSV file:
```python
import pandas as pd

data = pd.read_csv('your_airtable_data.csv')
# Perform any necessary data transformations here
```
Ensure the data types and structure align with the schema you plan to use in Iceberg.

Set up Apache Iceberg on your local or cloud environment. You will need to configure a data warehouse like Apache Hive or a compatible compute engine such as Apache Spark. Install Apache Iceberg following the official documentation for your chosen environment, ensuring all dependencies are correctly configured.

Convert your transformed data to a format compatible with Apache Iceberg. Start by saving your transformed CSV data into a Parquet format using pandas and PyArrow:
```python
import pyarrow as pa
import pyarrow.parquet as pq

table = pa.Table.from_pandas(data)
pq.write_table(table, 'transformed_data.parquet')
```
Parquet is a columnar storage file format that Iceberg supports and is efficient for analytical workloads.

Use Apache Spark to load your Parquet data into Iceberg. Start Spark with Iceberg configurations:
```shell
spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:latest.release
```
Within the Spark environment, execute a script to load the data:
```scala
val df = spark.read.parquet("transformed_data.parquet")
df.write.format("iceberg").mode("append").save("iceberg_table_name")
```
This command writes the data into an Iceberg table, appending it if the table already exists.

Finally, verify the data integrity to ensure a successful transfer. Use Spark or Hive SQL to query the Iceberg table and confirm that all records have been correctly imported:
```scala
val icebergData = spark.read.format("iceberg").load("iceberg_table_name")
icebergData.show()
```
Check for data consistency and accuracy by comparing a sample of the records against the original CSV data.