To start, you need to extract data from Chargebee. Chargebee provides an API that allows you to programmatically access your data. Use Chargebee’s REST API to export data by sending HTTP GET requests to the relevant endpoints (such as customers, subscriptions, invoices, etc.). Ensure you handle pagination and rate limiting as per Chargebee’s API documentation.

Once the data is extracted, transform it into a format compatible with Apache Iceberg, like Parquet, Avro, or ORC. Use a scripting language like Python or a tool like Apache Spark to read the JSON data from Chargebee and write it to the chosen format. This step involves data cleaning and ensuring that fields are correctly mapped and typed.

Before importing data, ensure you have an Apache Iceberg environment set up. This involves configuring your preferred compute engine that supports Iceberg (such as Apache Spark, Flink, or Hive) with the necessary Iceberg dependencies. You can set this up locally or on a cloud-based platform depending on your infrastructure needs.

Define an Iceberg table schema that matches the structure of the transformed data. This can be done using SQL commands in your compute engine’s environment. Ensure that the schema accurately reflects the data types and structures of the data extracted from Chargebee to prevent any inconsistencies.

With the data formatted and the schema defined, load the data into your Iceberg table. Use your compute engine to write the data files (Parquet, Avro, ORC) into the Iceberg table. This typically involves using SQL commands or programmatically loading the data through scripts or applications configured to interact with Iceberg.

After loading the data, perform a validation step to ensure data integrity and accuracy. Query the Iceberg table to check that all records are present and fields are correctly populated. This step is crucial to verify that the data migration process has been successful and that the data is ready for analysis.

Finally, consider automating the extraction, transformation, and loading (ETL) process for regular data updates. Use cron jobs, scripts, or continuous integration tools to schedule the ETL tasks, ensuring that your Iceberg tables stay up-to-date with Chargebee data. Document the automated process and include error handling and logging for monitoring purposes.

By following these steps, you can efficiently migrate data from Chargebee to Apache Iceberg without relying on third-party connectors or integrations.