To begin, use the Google Search Console API to extract data. You will need to authenticate using OAuth 2.0 and utilize the Search Console API to programmatically retrieve data such as search analytics, site maps, and more. Write a custom script in a language like Python to automate this extraction process and store the data in a structured file format such as CSV or JSON.

Once the data is extracted, transform it into a format compatible with Apache Iceberg. This may involve cleaning the data, normalizing date formats, and ensuring consistent column headers. You can use Python scripts with libraries like Pandas to read your CSV or JSON files, process the data, and then save it in a Parquet format, which is optimal for use with Iceberg.

Install and configure Apache Iceberg on your local machine or server. Ensure you have a working environment with Apache Hadoop or Apache Spark, as Iceberg is generally used within these ecosystems. Follow the official Iceberg documentation to set up the necessary configurations and dependencies.

With your Parquet files ready, create an Iceberg table. Write a Spark job using PySpark or Scala to load the Parquet data into the Iceberg table. Define the schema and partitioning strategy according to your data requirements. This step involves defining the table structure and executing the necessary Spark commands to ingest the data.

After loading the data into Iceberg, run a series of checks to ensure data integrity. This includes verifying row counts, checking for nulls, and ensuring data types match the expected schema. Use SQL queries in Spark or tools like Apache Hive to perform these checks.

Establish a mechanism for regularly updating the data in your Iceberg tables from Google Search Console. Automate the data extraction, transformation, and loading process using cron jobs or any preferred scheduling tool to keep the data up to date. This may involve incremental data loads or full reloads depending on your data volume and change frequency.

Regularly monitor the performance of your Iceberg tables. Use Iceberg's built-in capabilities to optimize data layout, such as compacting small files and managing partitions. Ensure that your system resources are adequately provisioned for the data volume and query load to maintain efficient performance.

By following these steps, you can successfully move data from Google Search Console to Apache Iceberg without relying on third-party connectors or integrations.