Amazon Athena is a serverless interactive query service that allows you to use standard SQL to analyze data directly in Amazon S3. The pricing model for Athena is based on the amount of data scanned by each query, and as of my last update, the cost is typically per terabyte (TB) scanned. The pricing might differ based on the region or recent updates, so it is wise to check the latest pricing details on the official AWS Athena pricing page.

To optimize costs and reduce the amount of data scanned per query, you can implement several strategies:

### 1. **Data Compression:**
– **Use Compression Algorithms:** Compress your data using efficient compression algorithms like Gzip, Snappy, or Zlib. Compressed data means less data is scanned during queries, resulting in lower costs.
– **Balance Between Compression and Decompression Costs:** Choose a compression format that balances the cost of scanning less data with the computational overhead required to decompress that data.

### 2. **Partitioning:**
– **Partition Your Data:** Partition data based on commonly filtered columns such as date, region, or other logical divisions. This allows Athena to only scan the partitions needed by the query, considerably reducing the amount of data scanned.
– **Use Logical Partitioning:** Ensure that your partition keys are chosen to optimize query performance and not just by arbitrary criteria. Every partition should be large enough to yield a distinct performance benefit.

### 3. **Columnar Storage Formats:**
– **Adopt Columnar Formats:** Store your data in columnar formats like Apache Parquet or ORC, which help reduce the amount of data scanned by only selecting the necessary columns for queries.
– **Column Pruning:** These formats allow for column pruning, which means only the required columns are fetched, reducing data scanned and enhancing performance.

### 4. **Projection/Attribute Selection:**
– **Select Specific Columns:** Instead of querying all columns with `SELECT *`, specify only the columns you need. This reduces the data scanned further, especially effective when using columnar storage formats.

### 5. **Efficient Query Development:**
– **Filter by Partitions/Efficiently:** Use WHERE clauses appropriately to filter data early in your query using partition columns to minimize the dataset to be scanned.
– **Optimize Query Logic:** Evaluate your query structure for optimization opportunities, like reducing the complexity of expressions and avoiding unnecessary data movement.

### 6. **Optimize Data Organization:**
– **Optimize Data Layout:** Organize your data in a way that optimizes scanning efficiency. For instance, combining small files into larger ones can sometimes help improve query performance and reduce overhead costs.
– **Use ETL to Pre-process Data:** Pre-aggregate or filter data before querying, using AWS Glue or another ETL tool, so Athena won’t need to scan through detailed, raw data.

By implementing these strategies, you can significantly optimize both the performance and cost of using Amazon Athena for your data analysis tasks. It is also beneficial to regularly review and adapt these practices as your data and query patterns evolve.