How to Efficiently Store and Query Big Data in AWS S3 Using Parquet and Athena

Handling big data efficiently is one of the biggest challenges in modern data engineering. AWS S3, combined with Athena and the Parquet file format, provides a powerful and cost-effective solution for storing and querying large datasets. S3 serves as a scalable data lake, while Athena enables serverless, SQL-based querying without the need to manage infrastructure. However, to make the most of this setup, it’s essential to structure data properly, use partitioning, and choose the right file format.

In this guide, we’ll explore why storing data in S3 with Parquet is more efficient than traditional formats like CSV or JSON, how to optimize data organization for faster queries, and how to leverage Athena to run cost-effective analytics.

Photo by Luke Chesser on Unsplash

Why Store Data in AWS S3?

AWS S3 is a popular choice for big data storage due to its scalability, durability, and cost-effectiveness. Unlike traditional databases that require expensive infrastructure, S3 allows you to store massive datasets at a fraction of the cost. Its pay-as-you-go pricing model ensures you only pay for what you store and query, making it ideal for data lakes.

Another key advantage of S3 is its seamless integration with various AWS services. It works well with AWS Glue for schema management, Redshift Spectrum for data warehousing, and Athena for querying large datasets directly without the need for an ETL process. With 99.999999999% durability (11 nines), data stored in S3 is highly reliable and automatically replicated across multiple availability zones.

Why Use Parquet Instead of CSV or JSON?

The choice of file format plays a crucial role in data storage and query performance. Many organizations still rely on CSV or JSON for storing structured data, but these formats are inefficient for large-scale analytics. CSV files store data row by row, requiring the entire file to be read even if only a few columns are needed. JSON, while more flexible, suffers from high storage costs due to its verbose structure.

Parquet, on the other hand, is a columnar storage format that significantly improves query performance and reduces costs. It allows queries to scan only the necessary columns instead of loading the entire dataset into memory. This reduces I/O operations and speeds up analytics. Additionally, Parquet supports built-in compression (e.g., Snappy, Gzip), which reduces storage size and enhances query efficiency. Unlike CSV, Parquet also supports schema evolution, allowing modifications without breaking existing queries.

For example, in a dataset containing millions of customer transactions, a query that retrieves only the customer_id and purchase_amount columns will run significantly faster with Parquet than with CSV, as Parquet reads only those columns instead of scanning every row.

How to Store Data Efficiently in S3

To fully leverage S3 and Athena, data must be structured correctly. A key strategy is partitioning, which organizes data into subdirectories based on frequently queried fields. A common partitioning strategy is by date, creating a structure like:

s3://your-bucket-name/dataset_name/year=2024/month=02/day=12/

This structure ensures that queries filtering by a specific date only scan relevant partitions instead of the entire dataset, significantly reducing query costs. Partitioning can also be done by region, customer segment, or any high-cardinality field relevant to your use case.

Another crucial factor is file size optimization. Ideally, each Parquet file should be between 128MB and 512MB to balance performance and cost. Too many small files increase metadata overhead and slow down queries, while excessively large files reduce parallel processing efficiency. AWS Glue Jobs can be used to consolidate small files into optimal-sized Parquet files.

Compression further enhances storage efficiency. Parquet supports multiple compression algorithms, with Snappy being the most commonly used due to its balance between speed and compression ratio. Gzip provides better compression but slightly slower decompression, making it suitable for cold storage scenarios where queries are less frequent.

AWS Glue can automate schema management by crawling Parquet files and registering them in the AWS Glue Data Catalog, allowing Athena to query them without manual schema definitions. This simplifies data governance and ensures consistency across datasets.

Querying Data in Athena

Once data is stored efficiently in S3, Athena can be used to query it using standard SQL. Since Athena operates on a pay-per-query model, optimizing queries to scan less data reduces costs.

To start querying Parquet data in Athena, create an external table linked to your S3 dataset:

CREATE EXTERNAL TABLE my_dataset (
    id STRING,
    name STRING,
    event_time TIMESTAMP,
    amount DOUBLE
)
PARTITIONED BY (year INT, month INT, day INT)
STORED AS PARQUET
LOCATION 's3://your-bucket-name/dataset_name/';

After creating the table, you must repair partitions to let Athena recognize them:

MSCK REPAIR TABLE my_dataset;

This ensures that new partitions added to S3 are included in queries. When running queries, always use partition filters to minimize data scanning. Instead of:

SELECT name, amount FROM my_dataset;

Use:

SELECT name, amount FROM my_dataset WHERE year = 2024 AND month = 02 AND day = 12;

This approach drastically reduces query costs by scanning only the necessary partitions rather than the entire dataset.

Performance Optimization Tips

To maximize efficiency and minimize costs when querying Parquet data in Athena, consider the following best practices:

• Enable Athena Query Result Caching: Athena caches query results for 45 minutes, allowing repeated queries to execute instantly without additional cost.
• Avoid Small Files in S3: Merge small files into larger ones using AWS Glue Jobs or Apache Spark to improve performance.
• Leverage AWS Cost Explorer: Regularly analyze Athena query costs and adjust partitioning strategies accordingly.
• Use AWS Lake Formation for Access Control: Implement fine-grained permissions to restrict access to sensitive data within the S3 data lake.

Conclusion

Storing and querying big data efficiently requires the right combination of tools and best practices. AWS S3, combined with Athena and the Parquet format, provides a scalable and cost-effective solution for data storage and analytics. By using partitioning, compression, and optimized query strategies, organizations can significantly reduce costs while improving query performance.

Implementing these best practices ensures that your data lake remains fast, efficient, and cost-effective, making it easier to extract valuable insights from large datasets. If you’re currently dealing with slow queries or high data storage costs, switching to Parquet and optimizing your Athena setup could be the solution you need.

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