Begin by logging into your Freshcaller account. Navigate to the analytics or reports section, where you can export data. Look for the option to download your call logs, customer records, or any other required data as CSV or Excel files. Ensure you select the correct date range and data fields before exporting. Save these files locally on your machine.

Open the exported files in a spreadsheet application or a text editor. Review the data to understand its structure and contents. Check for consistency in data types and formats, such as date formats or numerical representations. Clean the data by removing any unnecessary columns or correcting obvious errors. This step ensures that your data is ready for transformation into a format suitable for Apache Iceberg.

Apache Iceberg uses columnar storage formats like Parquet for efficient querying and storage. Use a data processing tool like Apache Spark or Python with Pandas to read the cleaned CSV files. Write a script to convert this data into Parquet format. For example, using Python and Pandas, read the CSV into a DataFrame and use the `to_parquet()` function to save it as a Parquet file.

Prepare an environment where Apache Iceberg can operate. This typically involves setting up an Apache Spark cluster or using a local environment with Apache Spark installed. Ensure that the Iceberg library is included in your Spark setup. You can add Iceberg to Spark by including it as a dependency in your Spark job or installing it in your local environment.

Define the schema for your Iceberg table that matches the structure of your transformed data. Use Apache Spark's SQL or DataFrame API to create a table schema in Iceberg. Make sure to specify the correct data types and primary keys, if applicable. This schema will guide how data is stored and queried in Iceberg.

With your data in Parquet format and an Iceberg table schema defined, load the data into Iceberg. Use Spark to read the Parquet files and write them into the Iceberg table. This can be done using Spark"s DataFrame API, specifying the Iceberg table as the target for the write operation. Ensure that the data is partitioned appropriately to optimize query performance.

After loading the data, perform validation checks to ensure data integrity and correctness. Run queries against the Iceberg table to verify that all expected records are present and that data fields match in terms of format and content. Check for any errors or discrepancies and rectify them by re-transforming and re-loading data if necessary.

By following these steps, you can efficiently move data from Freshcaller to Apache Iceberg without relying on third-party connectors, ensuring full control over the data flow process.