Before proceeding, familiarize yourself with the data format used by Whisky Hunter. Typically, this could be CSV, JSON, or another structured format. Access the dataset and note the structure and any relevant details such as column names, data types, and size.

Access the Whisky Hunter dataset and export it to a local file in a format you are comfortable working with, such as CSV or JSON. Ensure that all relevant data is captured during the export process.

Install and configure Apache Iceberg on your local machine or server environment. This involves setting up a compatible compute engine like Apache Spark or Apache Flink that supports Iceberg. Ensure that you have the necessary dependencies and configurations set up for Iceberg.

Using a scripting language like Python or a tool like Pandas, load the exported data from Whisky Hunter and preprocess it to ensure compatibility with Iceberg. This may involve cleaning the data, converting data types, or restructuring columns to fit the schema you plan to use in Iceberg.

Determine the schema for your Iceberg table based on the structure of your data. This involves specifying column names, data types, and any partitioning strategies you intend to use. Create the schema definition within your compute engine environment.

With the compute engine (e.g., Spark or Flink) set up, load the preprocessed data from your local file into an Iceberg table. This involves writing a script or using the engine’s command-line interface to insert the data into the Iceberg-managed table format. Ensure that the data is correctly mapped to the defined schema.

Once the data is loaded, perform a series of checks to verify data integrity. This includes running queries to ensure that the data is accurate, no data was lost, and the schema is correctly applied. Conduct tests to confirm that the data behaves as expected within the Iceberg table.

This guide assumes you have basic knowledge of data manipulation, scripting, and working with Apache Iceberg and its compatible computing engines.