When working with AWS Athena, an efficient querying process is essential to minimizing costs, as Athena charges based on the volume of data scanned. However, several common mistakes can lead to unnecessary cost increases, especially when handling large datasets. These errors often arise from inefficient query practices, misconfigurations, and lack of optimization. In this article, we'll explore the 10 most common mistakes that drive up your AWS Athena costs, along with practical strategies to address them. By understanding and mitigating these issues, you can optimize your query performance, reduce data scanning, and ultimately lower your AWS Athena expenses. Whether you're dealing with excessive data scans, improper partitioning, or inefficient joins, we've got you covered with actionable tips to enhance your Athena experience.

1. Excessive data scanning

Excessive data scanning is a common issue that leads to higher costs in platforms like AWS Athena, where charges are based on the volume of data read during query execution. This often happens when queries fail to apply the right filters to narrow down the dataset. For instance, neglecting to include a filter like a specific date range or region can cause Athena to scan the entire dataset, even if only a small subset is relevant to the query. This not only increases costs but also slows down query execution times. Additionally, selecting all columns instead of limiting the query to the required fields increases the data scanned unnecessarily, further inflating costs.

Common Table Expressions (CTEs) can compound this issue if not optimized. CTEs are often used for better readability and modular query design, but when they are defined without filters or involve selecting unnecessary columns, the query engine processes and scans more data than needed. Moreover, some platforms, including Athena, may not optimize repeated scans in CTEs effectively, leading to redundant data reads. To mitigate this, filters should be applied as early as possible within the CTEs, and only the required columns should be selected to reduce the amount of data processed and scanned, thereby controlling costs and improving query efficiency.

2. Lack of or poor Partitioning

Not using partitions or improperly configuring them can lead to significantly higher costs when querying data in platforms like AWS Athena, as the entire dataset must be scanned regardless of query filters. Partitioning organizes data into smaller, logical chunks (e.g., by date, region, or category) so that only the relevant partitions are read during a query. Without partitioning, even a query targeting a specific subset of the data (e.g., a single month) would require scanning the entire dataset, inflating data scanned costs and resulting in unnecessary expenses.

Improperly configured partitions, such as using overly granular partitioning (e.g., by minute) or unevenly sized partitions, can also cause inefficiencies. Over-partitioning increases metadata overhead and query planning time, while poorly chosen partition keys (e.g., static values) prevent effective pruning of irrelevant data. Proper partitioning dramatically reduces costs by limiting the data scanned. For example, querying a single day in a yearly dataset partitioned by date scans only 1/365th of the data, directly lowering both storage and processing expenses.

3. Raw file formats / Lack of compression

Based on the analysis of the NYC Yellow Taxi data, CSV and JSON file formats achieve an average compression ratio of 90–95% when compressed using algorithms such as Gzip or Zstandard (Zstd). This level of compression significantly reduces the size of the raw data, effectively shrinking it to 5–10% of its original size.

For example, a dataset that initially occupies 10 GB in its raw format can be reduced to just 500 MB to 1 GB after compression. This reduction in file size translates directly into lower storage costs, as less space is required to store the compressed data on cloud platforms or local infrastructure.

Moreover, compression also has a substantial impact on processing costs, especially in systems like AWS Athena, where costs are directly related to the amount of data scanned during queries. Compressed files require significantly less data to be read from storage, effectively reducing the amount of data scanned by a factor of 10 to 20. This optimization not only lowers query costs but also improves query execution times, enabling faster data retrieval and analysis.

4. Unoptimized formats

When working with raw file formats such as CSV or JSON, Athena must read the entire file to process the data. These formats do not offer any native indexing or optimization, meaning Athena has to parse every single row even if only a subset of the data is needed for the query. This results in inefficiencies, especially when working with large datasets, leading to higher query costs and slower performance. As a result, raw formats like CSV or JSON can be more resource-intensive, as Athena has to scan and deserialize the entire dataset before performing any operations.

In contrast, columnar formats like Parquet are optimized for querying large datasets. Parquet organizes data in columns rather than rows, which allows Athena to read only the relevant columns needed for a specific query. This significantly reduces the amount of data that needs to be read, improving both query performance and cost efficiency. Furthermore, Parquet files store metadata, including min/max values for each row group, enabling predicate pushdown. This allows Athena to skip irrelevant rows, thereby filtering out unnecessary data early in the process. Additionally, column statistics in Parquet files can be leveraged for aggregations, allowing Athena to perform operations such as COUNT, SUM, or AVG more efficiently by minimizing the amount of data that needs to be scanned.

5. Not implemented Lifecycle query rules

Failing to implement Amazon S3 lifecycle rules for managing Athena query data can lead to unnecessary storage costs over time. Each time a query is executed in Athena, the results are stored in an S3 bucket, typically in a default “query results” location. Without proper lifecycle management, these results can accumulate indefinitely, consuming valuable storage space and increasing costs, especially for high-frequency query workloads or large datasets.

By configuring S3 lifecycle rules, you can automatically transition older query results to more cost-efficient storage tiers, such as S3 Standard-IA (Infrequent Access), or delete them after a certain retention period. This ensures that only recent or critical query results are stored in higher-cost tiers, while unnecessary historical data is either removed or archived. Implementing such rules is a simple yet effective way to optimize storage costs and prevent long-term cost bloat from unmaintained query result data.

6. Repeating the same query

Repeating the same query multiple times in AWS Athena without optimizing for reuse can significantly increase costs. Athena charges based on the amount of data scanned, so running identical queries repeatedly — especially on large datasets — results in unnecessary data scanning and inflated expenses. This is common when queries are rerun for exploratory purposes or when automated systems trigger the same query due to poorly designed workflows or dashboards.

A similar issue arises when querying Athena directly from dashboards like Tableau or Power BI. These tools often execute queries dynamically to provide real-time results as users interact with the dashboard, applying filters or refreshing data. Without optimization, each action or filter applied can trigger a new query, potentially scanning large volumes of data repeatedly. This leads to high costs due to unnecessary data reads.

To mitigate both issues, several strategies can help optimize Athena queries:

1. Reuse Results: Save query results to S3 and use them directly for subsequent analysis, instead of re-scanning large datasets each time.
2. Materialized Views and Pre-Aggregated Data: Create materialized views or summarized datasets stored in optimized formats like Parquet to minimize data scanned in future queries.
3. Use Caching: Enable caching in your BI tools, such as Power BI, to store query results for a specified period and avoid repeated queries to Athena for the same data.
4. Effective Partitioning: Organize data in partitions (e.g., by date or region) to ensure that only relevant data is scanned, reducing the volume of data processed for repeated or dynamic queries.
5. Limit Dashboard Interactions: Restrict the complexity of user interactions in dashboards to avoid triggering expensive queries and instead rely on pre-processed or cached data when possible.

7. Using complex types / Nested data

Leveraging complex types or columns with nested data can significantly increase your Athena query cost. When a JSON field is included within a Parquet file (i.e., stored as a STRING or STRUCT with nested data), Athena treats it as a single column. If you query fields within the JSON (using JSON functions or path expressions like json_column.field_name), Athena needs to parse the JSON for every row that contains it. This introduces additional overhead since Athena will need to deserialize the entire JSON field, even if only a small part of the nested structure is needed.

For example: If you have a JSON field like {json_column: '{"id": 1, "name": "John", "age": 30}'} and query json_column.id, Athena will still need to parse the entire JSON field for each row to extract the id value.

Additionally Storing JSON as a single STRING or STRUCT field may reduce some opportunities for compression and optimization. JSON data often contains redundant or nested structures, which could reduce the effectiveness of Parquet’s columnar compression algorithms. In addition, since the JSON data is stored as a single field, Athena may not benefit from column-level compression for that field.

8. Column ordering & Sorting in columnar formats

In AWS Athena, the order of columns in Parquet files and the sorting of data can significantly impact both query performance and associated costs. When columns are not ordered optimally, Athena may need to scan unnecessary data, even if only a few columns are relevant for the query. This is particularly important because Athena uses column pruning to skip irrelevant columns during query execution. If frequently queried columns are placed later in the schema, Athena may not be able to efficiently skip over the unnecessary columns, leading to increased data scanning and higher costs. Additionally, Parquet’s compression is more effective when similar data is grouped together, so improper column ordering can result in less efficient compression, thus increasing the amount of data that Athena must read.

Similarly, the sorting of data within Parquet files plays a crucial role in query optimization. For instance, if queries frequently filter on date ranges or specific attributes, sorting the data on those fields can allow Athena to more effectively prune irrelevant partitions or rows. Without proper sorting, Athena may need to scan more data than necessary, as it might not be able to take full advantage of partition pruning or efficient data locality. As a result, both improperly ordered columns and unsorted data can lead to unnecessary data scans, which ultimately drive up the costs of querying with Athena. By ensuring optimal column ordering and sorting of data, organizations can significantly reduce Athena query costs while improving performance.

9. UNOPTIMIZED JOINS

Optimizing joins in Athena is crucial for minimizing query costs and improving performance, particularly when dealing with large datasets. One key strategy is to ensure that both tables involved in the join are partitioned on the same column. This reduces the amount of data that needs to be shuffled between nodes, as Athena can read only the relevant partitions, avoiding unnecessary data movement. Additionally, using efficient join types, such as INNER JOIN or EQUI JOIN, helps avoid the overhead of combining unmatched rows, which would otherwise result in increased data shuffling. Pre-filtering smaller tables before performing a join can further reduce the amount of data being processed, ensuring that only necessary data is shuffled.

Another way to optimize join performance is by using broadcast joins for small tables. In cases where one table is significantly smaller than the other, Athena can broadcast the smaller table to all nodes, avoiding the need for large-scale data shuffling. This strategy can be especially beneficial for look-up tables or reference data that do not need to be redistributed. Additionally, avoiding costly CROSS JOINS and filtering out unnecessary columns before the join can significantly reduce the volume of data involved, further minimizing shuffle overhead. By employing these techniques, you can ensure that your joins are efficient, reducing both execution times and associated costs in Athena.

10. Missing Proper Data Cleanup and Deduplication

One common reason for high Athena costs is the absence of proper data cleanup and deduplication in your datasets. When duplicate or irrelevant records exist, Athena must scan more data than necessary, leading to increased query costs. Regularly performing data cleanup to remove duplicate records, irrelevant rows, and inconsistencies before executing queries ensures that Athena processes only the necessary data. This reduces the amount of data scanned and speeds up the query execution, ultimately lowering costs. Proper data preprocessing helps Athena run more efficiently and reduces the risk of scanning excess data.

Deduplication should be implemented early in the query process to minimize the amount of data being processed downstream. For instance, applying a ROW_NUMBER() == 1 or using a DISTINCT clause early on in the query ensures that only unique rows are retained for further processing. By deduplicating early, you can ensure that subsequent stages of the query work with a smaller, cleaner dataset, reducing the overall data scanned and optimizing costs. This proactive approach to cleaning up data before the final query steps prevents unnecessary computations and improves the efficiency of the query execution.

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