What solution meets the requirements for cost optimization and data ingestion in Amazon S3 for a company with large amounts of incoming data?

Choosing to create an ETL job to compress, partition, and convert data into a columnar format is the most effective solution for meeting the requirements of cost optimization and efficient data ingestion in Amazon S3.

This approach is beneficial because compressing data reduces its storage footprint, thereby lowering costs associated with storing large volumes of incoming data. Furthermore, partitioning the data enhances query performance since it allows for more efficient access patterns, limiting the amount of data that must be read during queries, which can also lead to cost savings. Converting data into a columnar format, such as Parquet or ORC, is particularly advantageous for analytical workloads. These formats are designed to allow efficient data retrieval and are optimized for analytical queries as they reduce IO operations and storage costs significantly.

Other methods mentioned, such as storing raw data without lifecycle policies or relying solely on Amazon Athena to query raw data, do not address the cost optimization aspect effectively. While using S3 Lifecycle policies can help manage data costs, applying them to all data immediately without considering specific use cases or access patterns might lead to inefficiencies and unwanted cost implications. Thus, the ETL job approach strikes the best balance between optimizing costs and ensuring effective data handling in Amazon S3.