Amazon Redshift Spectrum vs. Athena:
A Detailed Comparison

6 Comparisons Between AWS Redshift Spectrum and AWS Athena

• Redshift Spectrum runs in tandem with Amazon Redshift, while Athena is a standalone query engine for querying data stored in Amazon S3.

• With Redshift Spectrum, you have control over resource provisioning, while in the case of Athena, AWS allocates resources automatically.

• The performance of Redshift Spectrum depends on your Redshift cluster resources and optimization of S3 storage, while the performance of Athena only depends on S3 optimization.

• Redshift Spectrum can be more consistent performance-wise while querying in Athena can be slow during peak hours since it runs on pooled resources.

• Redshift Spectrum is more suitable for running large, complex queries, while Athena is more suited for simplifying interactive queries.

• Redshift Spectrum needs cluster management, while Athena allows for a truly serverless architecture.

At a quick glance, Redshift Spectrum and Athena seem to offer the same functionality — serverless query of data in Amazon S3 using SQL. You don't need to maintain any infrastructure, which makes them incredibly cost-effective. However, the two differ in their functionality. Let's take a closer look at the differences between Amazon Redshift Spectrum and Amazon Athena.

Redshift Spectrum is an extension of Amazon Redshift. The service allows data analysts to run queries on data stored in S3. It makes it possible, for instance, to join data in external tables with data stored in Amazon Redshift to run complex queries.

For more information on Integrate.io's native Redshift connector, visit our Integration page.

Amazon Athena, on the other hand, is a standalone query engine that uses SQL to directly query data stored in Amazon S3. Much like Redshift Spectrum, Athena is serverless. There is no need to manage any infrastructure.

Functionality and Performance Comparison for AWS Redshift Spectrum vs. AWS Athena

Both the services use Glue Data Catalog for managing external schemas. They use virtual tables to analyze data in Amazon S3. However, in the case of Athena, it uses Glue Data Catalog's metadata directly to create virtual tables. With Redshift Spectrum, on the other hand, you need to configure external tables for each external schema.

A key difference between Redshift Spectrum and Athena is resource provisioning. In the case of Athena, the Amazon Cloud automatically allocates resources for your query. You do not have control over resource provisioning. Thus, performance can be slow during peak hours. When using Spectrum, you have control over resource allocation since the size of resources depends on your Redshift cluster. Thus, if you want extra-fast results for a query, you can allocate more computational resources to it when running Redshift Spectrum.

AWS Redshift Spectrum and AWS Athena are compatible with AWS Glue, the serverless computing platform provided as a part of Amazon’s web services. You can use AWS Glue Data Catalog as the metadata repository for AWS Redshift Spectrum, which serves as a trusted data storage across your network.

Amazon recommends that AWS Athena users upgrade their internal data catalog with AWS Redshift Spectrum to AWS Glue, which can help optimize cost and performance in the long term through improved capabilities. These include maintained scheme visioning and ETL capabilities of converting data into columnar file formats for faster analytics workloads. According to tests, columnar formats have shown cost-effectiveness and faster performance compared to row-based file formats.

Additionally, several Redshift clusters can access the same data lake simultaneously. However, you can only analyze data in the same AWS region.

Arrange for a call with Integrate.io to discover the most suitable query engine to optimize your data management processes. 

AWS Redshift Spectrum vs. AWS Athena Integrations

Athena has prebuilt connectors that let you load data from sources other than Amazon S3. Athena can connect to Redis, Elasticsearch, HBase, DynamoDB, DocumentDB, and CloudWatch. If you want to analyze data stored in any of those databases, you don't need to load it into S3 for analysis. You can run your queries directly in Athena.

Redshift uses Federated Query to run the same queries on historical data and live data. More importantly, with Federated Query, you can perform complex transformations on data stored in external sources before loading it into Redshift. ETL is a much more secure process than ELT, especially when sensitive information is involved.

AWS Redshift Spectrum vs. AWS Athena Use Cases

AWS Redshift Spectrum and AWS Athena provide varying query services according to specific data management needs in real-time. For instance, you might consider AWS Athena for serverless VPCs (virtual private clouds).

AWS Redshift Spectrum

AWS Redshift Spectrum expands the analytic powers within Amazon Redshift. As such, you can effortlessly apply AWS Redshift Spectrum for added data management implementations. For instance, AWS Redshift Spectrum can help improve the interoperability of S3 data by providing accessibility to multiple compute formats outside Amazon Redshift.

With AWS Redshift Spectrum, you can effectively optimize your querying and scaling process across nodes that optimize your networks' performance.

Essentially, AWS Redshift Spectrum enables you to optimize your workloads as a serverless compute service. Running multiple operations outside of AWS Redshift reduces the computational load on AWS Redshift, ultimately improving the concurrency, performing markedly better than native AWS Redshift in some use cases.

AWS Redshift Spectrum allows you to share data sets across AWS Redshift clusters by creating external tables. Querying each table with AWS Redshift Spectrum reduces the risk of data duplication while providing a consistent user view across the shared data. These processes can help streamline and optimize multi-tenant use cases involving multiple separate data clusters.

AWS Athena

AWS Athena serves as an efficient solution for analyzing unstructured and structured S3 data, such as CSV, JSON, and columnar formats that include Apache ORC. With AWS Athena, you can readily generate insightful reports by integrating with leading business intelligence tools to provide detailed data analytics. Additionally, you can conveniently integrate AWS with a data visualization tool like Amazon Quicksight that offers an improvement to standard database indexes.

You can apply AWS Athena in querying data from AWS EMR, a cloud big data platform that makes it cost-effective to manage large-scale, highly distributed data frameworks such as Presto and Hadoop. The flexibility of AWS EMR can provide your systems with efficient management of data streaming, machine learning, data transformations, and graph analytics.

Since AWS works well with many similar data types as AWS EMR, you can query your multiple data sources in a frictionless process.

AWS Redshift Spectrum vs. Athena Cost Comparison

Both services follow the same pricing structure. You only pay for the queries you run. The total cost is calculated according to the amount of data you scan per query. The cost of running queries in Redshift Spectrum and Athena is $5 per TB of scanned data.

It is important to note that you need Redshift to run Redshift Spectrum. If you are not an Amazon Redshift customer, running Redshift Spectrum together with Redshift can be very costly.

More importantly, consider the cost of running Amazon Redshift together with Redshift Spectrum. The cost of running Redshift, on average, is approximately $1,000 per TB, per year.

AWS Amazon Redshift Spectrum vs. AWS Athena: Which One to Choose?

The two services are very similar in how they run queries on data stores in Amazon S3 using SQL. For example, each solution queries with S3 through standard SQL, and you will need to optimize your S3 storage layers to optimize the performance of both querying systems. However, to decide between the two, consider the following factors:

1) Are You an Existing Redshift Customer?

For existing Redshift customers, Spectrum might be a better choice than Athena. They can leverage Spectrum to increase their data warehouse capacity without scaling up Redshift. It can help them save a lot of dollars.

For example, you can store infrequently used data in Amazon S3 and frequently stored data in Redshift. Doing so reduces the size of your Redshift cluster and, consequently, your annual bill while you manage workloads efficiently.

Quick Start

You can get started on Amazon Redshift Spectrum in a few easy steps. Firstly, you will need to create an IAM role for Amazon Redshift, which authorizes your cluster size. You will need to associate the IAM role with your Amazon Redshift cluster, which provides access to the external data catalog and S3 data.

It is important, though, to keep in mind that you pay for every query you run in Spectrum. If your team of analysts is frequently using S3 data to run queries, calculate the cost vis-a-vis storing your entire data in Redshift clusters.

If you are not a Redshift customer, Athena might be a better choice. You don't need to maintain any clusters with Athena. You can build a truly serverless architecture.

2) Compatibility With Your Analytic Tools

Before you choose between the two query engines, check if they are compatible with your preferred analytic tools. Both the services use OBDC and JBDC drivers for connecting to external tools.

3) Budget Considerations for Improved Performance

Because AWS Redshift Spectrum has a generally more consistent performance compared to AWS Athena, since it does not tap on a pool of shared data resources like AWS Athena, it might require a higher cost. Specifically, you might need to expand your core AWS Redshift clusters to accommodate the increased compute usage if needed.

How Can Integrate.io Help?

Integrate.io lets you build ETL data pipelines in no time. The Integrate.io platform functions as an ultra-fast CDC platform with reverse ETL capabilities to optimize your e-commerce businesses. Using the visual interface, you can quickly start integrating Amazon Redshift, Amazon S3, and other popular databases.

Schedule a call and learn how our low-code platform makes data integration seem like child's play.