Begin by utilizing Mixpanel's API to extract the data you need. Mixpanel provides a powerful API that allows you to programmatically access your event data. Use the Export API to pull data in JSON or CSV format. You can write a script in Python, Node.js, or any language of your choice to handle the HTTP requests, authenticate using your project token, and handle pagination if needed.

Once you have the data extracted from Mixpanel, it needs to be transformed into a format compatible with Apache Iceberg, such as Parquet or ORC. Use a data processing tool or write a script to convert the JSON or CSV files into Parquet files. Libraries such as Apache Arrow (PyArrow for Python) can be used to read JSON/CSV and write Parquet files programmatically.

If not already set up, install and configure Apache Iceberg. This typically involves setting up a compatible compute engine like Apache Spark or Flink that supports Iceberg. Ensure that your environment is configured to read from and write to Iceberg tables. Install necessary Iceberg connectors and ensure your Spark session is configured to use Iceberg.

Define the schema of your Iceberg table. Based on the data extracted from Mixpanel, create a schema that matches the structure of your data. This schema definition will be used when creating the table in Iceberg. You can use SQL within your chosen compute engine (e.g., Spark SQL) to define and create tables.

With your data in Parquet format and the Iceberg table schema defined, use Spark or another compute engine to load the data into Iceberg. This can typically be done using Spark SQL commands to read the Parquet files and write them into the Iceberg table. Ensure you specify the Iceberg catalog and table name correctly to direct the data appropriately.

After loading the data, perform checks to ensure data integrity and completeness. Query the Iceberg table to verify that the data matches what was extracted from Mixpanel. You can perform counts, checksums, or sample queries to validate that the data is accurate and complete.

Once you have manually confirmed that the data movement process works correctly, consider automating it for efficiency. Write scripts to automate extraction, transformation, and loading (ETL) tasks, and schedule them using a job scheduler like cron or Airflow. This will ensure your data is kept up-to-date without manual intervention, provided there are no third-party tool constraints.

By following these steps, you can effectively move data from Mixpanel to Apache Iceberg without relying on third-party connectors.