Before initiating the data migration process, thoroughly assess the structure of the data stored in IBM Db2. Identify the tables, views, and any specific data types or constraints that may need special handling. Understand the target schema in Apache Iceberg to ensure compatibility and plan for any necessary transformations.

Use the Db2 export utility to extract data from the database. You can execute SQL queries to export data into CSV or another delimited format. For example, use a command like `EXPORT TO 'data.csv' OF DEL MODIFIED BY NOCHARDEL SELECT FROM tablename;` to export tables. Ensure that the exported data is in a format that can be easily ingested by Apache Iceberg.

Set up your Apache Iceberg environment. Ensure that you have the necessary infrastructure, such as a Hadoop or Spark cluster, to manage the Iceberg tables. Create the target table structure in Iceberg using a SQL-like interface or via programmatic means, ensuring it matches the structure of the data exported from Db2 or is appropriately transformed.

Move the exported data files from where they were created to the storage location accessible by your Iceberg setup. This could involve transferring files to a Hadoop Distributed File System (HDFS) or a cloud storage solution that your Iceberg environment can read from.

Use an Apache Spark job or any compatible processing tool to read the exported data files and write them into Apache Iceberg tables. For example, in Spark, you can read CSV files using `spark.read.csv("path/to/data.csv")` and then write to Iceberg using `write.format("iceberg").mode("append").save("iceberg_table_name")`. Ensure that any necessary data transformations are performed during this step.

Once the data is ingested into Iceberg, perform thorough checks to ensure data integrity and consistency. This may include counting records, checking for null values, and verifying data types and constraints. Compare these results with the original data in Db2 to ensure accuracy.

Post ingestion, optimize the Iceberg tables to enhance performance. This involves compacting small files, reordering data, and building any necessary indexes. Use Iceberg's built-in features to perform these tasks, ensuring that your data is structured for efficient querying and analysis.