Begin by accessing the Yandex Metrica API to extract the necessary data. You will need to create an application in Yandex and obtain an OAuth token for authentication. Use the Yandex Metrica Reporting API to define the data you wish to extract, specifying the date range and metrics. Write a script in a language like Python or Java to send requests to the API and retrieve the data in a format such as JSON or CSV.

Once you've extracted the data, store it locally on your system. You can write the data to a file, such as a CSV or JSON file, depending on what format you retrieved it in. This step ensures you have a backup of the raw data and allows for easier manipulation and transformation in subsequent steps.

Analyze the schema requirements for your Apache Iceberg table and transform the extracted Yandex Metrica data to match this schema. Use a data processing tool or write a script in a language like Python, using libraries such as Pandas, to clean, format, and transform the data. Ensure that data types are compatible and that all necessary fields are included.

Prepare your Apache Iceberg environment. Ensure that you have a compatible compute engine set up, such as Apache Spark or Apache Flink, to interact with Iceberg. Configure your environment to connect to your data lake storage, using services such as AWS S3, Azure Blob Storage, or HDFS, where Iceberg tables will be stored.

Define the schema for your Apache Iceberg table. This involves specifying the table name, column names, data types, and any partitioning strategy that might be needed for your data. Use your chosen compute engine's Iceberg integration to execute the schema creation command, ensuring it aligns with the transformed data format.

Use your compute engine to load the transformed data into the Apache Iceberg table. This can be done through a data import job, where you read the local file containing the transformed data and write it into the Iceberg table. For example, if using Apache Spark, you can use the Spark DataFrame API to read the file and then write it to the Iceberg table.

After loading the data, verify the data integrity and consistency. Run queries against the Iceberg table to ensure that all data has been correctly loaded and matches the expected schema. Perform checks for data completeness and accuracy by comparing sample records with the original Yandex Metrica data. Adjust any discrepancies as needed.

By following these steps, you can systematically move data from Yandex Metrica to Apache Iceberg without relying on third-party connectors or integrations, ensuring a controlled and customizable data pipeline.