AWS Athena vs. Redshift Spectrum - Which One Should You Choose?

We've done the comparison, so you don't have to

May 15, 2023

AWS Athena and AWS Redshift Spectrum are query services offered by Amazon Web Services (AWS) for processing and analysing large amounts of data in a cost-effective and efficient manner. Although both services offer similar capabilities, there are some key differences that set them apart.

Service infrastructure

AWS Athena is a serverless interactive query service that allows users to analyse data stored in Amazon S3 using standard SQL queries. It does not require any infrastructure setup or management and is designed to handle ad hoc queries and data exploration.

Redshift Spectrum, on the other hand, is a part of Amazon Redshift, a data warehouse service also offered by AWS, and can’t be used independently. Redshift Spectrum allows users to query data stored in both Amazon S3 and Redshift clusters using SQL, providing a unified view of all data.

Resource provisioning

AWS Athena provides no control over resource provisioning. It uses pooled resources as allocated by AWS, which means that performance can vary based on usage. You may experience a drop in performance, especially during peak usage times due to the sharing of resources. In essence, the only way to increase Athena performance is by optimising data (partitioning, compression, etc.).

The resources for Redshift Spectrum are assigned according to the size of the Redshift cluster. This means that you can theoretically increase Redshift performance by adding resources to Redshift. When you need a boost in power, e.g., when results are needed quickly or while running large, complicated queries, you can increase the Redshift cluster size, even though this will be expensive in the long run.

Use cases for optimal performance

AWS Athena is optimised for ad hoc queries and interactive analysis, and can return results within seconds or minutes. With Athena, users can create tables and views, perform complex joins, and use window functions to analyse data. However, Athena may not be as performant as Redshift Spectrum for complex queries and large datasets.

Redshift Spectrum, being an extension of Amazon Redshift, can handle large workloads and can provide fast query performance on very large datasets. It can handle complex joins and bigger aggregations with ease. However, the performance of Redshift Spectrum largely depends on the cluster size and configuration, and can be more expensive to operate than Athena.

Supported file formats

Athena supports various data formats like CSV, TSV, JSON, or Textfiles and open-source columnar formats, such as ORC and Parquet. Athena also supports compressed data in Snappy, Zlib, LZO, and GZIP formats.

The file formats supported in Amazon Redshift Spectrum include CSV, TSV, Parquet, ORC, JSON, Amazon ION, Avro, RegExSerDe, Grok, RCFile, and Sequence. It also supports compressed data in Gzip, Snappy, LZO, BZ2, and Brotli (only for Parquet).

For both these services, you can improve performance and reduce costs by compressing and using columnar formats.

Schema management

Athena for SQL uses a managed AWS Glue Data Catalog to store information and schemas about the databases and tables that you create for your data stored in S3. In regions where AWS Glue is not available, Athena uses an internal catalog. Redshift Spectrum also uses AWS Glue Data Catalog, but not directly. You need to configure external tables as per each schema of the Glue Data Catalog.

Pricing model

Athena pricing is based on the amount of data scanned during query execution, which means that users only pay for the data they access. Redshift Spectrum is priced based on the number of nodes in the Redshift cluster that is used to process queries. This means that users pay for the infrastructure used, regardless of the amount of data queried. Therefore, Athena can be more cost-effective for ad hoc queries and small workloads, while Redshift Spectrum is more suitable for larger, ongoing workloads.

Making the right choice

Both AWS Athena and AWS Redshift Spectrum are powerful query services that provide users with the ability to analyse large amounts of data stored in Amazon S3 and Redshift clusters. The choice between them largely depends on the specific use case and workload. Athena is a good option if you're a beginner and just beginning your data journey. It is cost-effective and works best with ad hoc queries and small workloads. If you're already using AWS Redshift, then it makes more sense to choose Redshift Spectrum. It is also more suitable for larger, ongoing workloads.

Another alternative is ByteHouse, developed by ByteDance. One advantage that ByteHouse offers over Athena and Redshift Spectrum is that it combines the features of both a cloud query service and a data warehouse. Being a versatile data platform, it can directly query data from Amazon S3. You don't need to invest in separate services to perform these data processing tasks. ByteHouse delivers lightning-fast performance even with complex schemas, where its competitors lag.

Athena vs. Redshift Spectrum Summary



Redshift Spectrum

Type of service


Part of Redshift

Designed for

Querying data stored in Amazon S3

Querying data stored in both Amazon S3 and Redshift clusters

Optimal use cases

Basic table scans, small aggregations, ad hoc queries.

Complex joins and bigger aggregations

Resource provisioning

No control over resource provisioning. Pooled resources allocated by AWS, can vary based on usage esp. peak usage times

Some control over resource provisioning. Dependant on Redshift cluster size, can increase Redshift cluster size to boost compute power

Schema management

Designed to work directly with AWS Glue for schema management

Uses AWS Glue, needs external tables to be configured for each Glue catalog schema


$5 per compressed terabyte scanned

$5 per compressed terabyte scanned; , More expensive in general as it also uses Redshift compute resources for consistent performance.

AWS Athena
AWS Redshift
Redshift Spectrum
Query services
Related articles

How to Unravel the Intertwined Relationship between Big Data and IoT

SQL and NoSQL databases: What are the types and ideal use cases

Data modelling: Understand the benefits and improve your business