- 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
- 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, both, 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 Xplenty'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.
Table of Contents:
- Functionality and Performance Comparison for Redshift Spectrum vs. Athena
- Redshift Spectrum vs. Athena Integrations
- Redshift Spectrum vs. Athena Cost Comparison
- Amazon Redshift Spectrum vs. Athena: Which One to Choose?
Functionality and Performance Comparison for Redshift Spectrum vs 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.
Additionally, several Redshift clusters can access the same data lake simultaneously. However, you can only analyze data in the same AWS region.
Redshift Spectrum vs. 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 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 compared to ELT, especially when there is sensitive information involved.
Related reading: ETL vs ELT
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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.
Amazon Redshift Spectrum vs. Athena: Which One to Choose?
The two services are very similar in how they run queries on data stores in Amazon S3 using SQL. 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.
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.
How Can Xplenty Help?
Xplenty lets you build ETL data pipelines in no time. 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.