We’ll take a close look at Athena and Spectrum here, with the aim of helping you understand when to use them for different types of analytics tasks.
Considering their various use cases, Athena and Redshift Spectrum make excellent choices.
Cost Difference between Redshift and Athena
When running a query in Spectrum, the amount of data scanned is billed according to how much data is scanned. AWS rounds up to the nearest megabyte, so you’ll always pay at least $5 per query. If your 10 MB free trial expires without any charges applied to your account, Athena will charge you based on how much data was scanned.
S3 storage would be another cost to consider since it is relatively inexpensive compared to databases. Since these services are decoupled so that storage and computation are separated, you can make use of inexpensive S3 to handle petabyte or exabyte-scale data without racking up massive cloud fees.
While these costs are all-inclusive in Athena, they are also all-inclusive for Spectrum – as we will cover later, you will have to allocate these costs based on your cluster of Redshift servers.
Difference in Performance of Redshift and Athena
Both Spectrum and Athena are serverless but differ in that Athena uses pooled resources from Amazon Web Services (AWS) for queries, whereas Spectrum allocates resources depending upon the number of nodes within an RDS instance.
Redshift Spectrum, therefore, gives you greater control over performance. In cases where you need a query to return extra-fast, you can allocate additional compute resources (unfortunately, this can get costly over time). Athena, on the other hand, uses the resources allocated automatically by AWS, which might differ during peak usage periods.
When querying data stored on Amazon S3, Spectrum and Athena both use virtual tables. These tables are managed using Glue Data Catalog. In Athena, table metadata is stored directly in the Glue Data Catalog. For each Glue Data Catalog schema, external tables must be configured when using Redshift Spectrum.
Essentially, both Athena and Redshift Spectrum do the same thing: query S3 using standard SQL, and store the results. There is only one major difference between Athena and Spectrum: Athena stores query results on S3, which can be loaded into Redshift from there; while Spectrum can join tables directly on Redshift.
Redshift Spectrum and Athena are both serverless applications. They store their data on Amazon S3, have no need for an index, and cannot perform joins. However, if you’re joining two tables with a high correlation then the ETL layer of your process will execute that join automatically.
Connectors to external services
With Athena, you are able to load data from external sources other than S3 directly into the database, so you do not have to copy it to S3 beforehand. The full list of Redshift connectors can be found here. With Redshift Federated Query, you can run a query on historical data stored in Redshift or S3, and live data stored in Amazon RDS or Aurora.
Redshift can also be ingested using Federated Query. By querying operational databases, the service allows you to perform transformations and then load data directly into Redshift tables.
Choosing between Redshift Spectrum and Athena
Amazon Athena and Redshift Spectrum are similar-yet-distinct services, as we’ve seen. Athena and Spectrum both use serverless engines to query Amazon S3 data, but Athena is an interactive service, whereas Spectrum is part of the Redshift stack. Which workload should go where?
You should consider Redshift Spectrum if you need your queries to be closely tied to a Redshift data warehouse. A Redshift table can be created by joining S3 data with Redshift data. Spectrum makes it easy to do this.
If you have all your data in S3, you should consider Athena. It is probably not worth the effort and cost to spin up a Redshift cluster just to use Spectrum if you are not looking to analyze Redshift data. Instead, use Athena to read from S3.
You may want to consider Redshift Spectrum if you are willing to pay more for better performance. Spectrum’s performance is more consistent because it doesn’t use pooled resources, as we discussed in the previous section. You might have to pay more for a larger cluster if this increases your Redshift compute usage.
FAQs about Redshift Spectrum and Athena
Is Redshift spectrum faster than Athena?
Redshift Spectrum generally faster than Athena as it uses dedicated resources that are allocated based on the size of the Redshift cluster. Redshift Spectrum is a fast and scalable data warehouse that can be queried using SQL or Python. It was designed for customers who are running complex queries against petabyte-scale datasets.
When should I use Redshift spectrum over Athena?
One of the main reasons to use Redshift spectrum over Athena is that it allows you to do more complex queries, which are not possible with Athena. The other main reason why Redshift spectrum should be used over Athena is when one needs to handle larger datasets. For example, if you have a dataset of 10 million rows, then your query will take 5 minutes on Athena but only 30 seconds on Redshift Spectrum.
Why use Athena over Redshift?
Athena is best suited when you need fast, interactive queries on small to medium sized datasets. It is free, and it is easy to learn. It also has many features that Redshift Spectrum does not have such as the ability to run SQL queries over data in S3 buckets, which allows for more flexibility than what Redshift Spectrum offers when querying data from Amazon S3 buckets.
Which One to Choose Between Redshift Spectrum and Athena
The best way to make this decision is to compare the two tools in terms of their features, price, and limitations. Athena has a lot of advantages over Redshift Spectrum. It is cheaper, has a better interface and it can be used for data exploration. However, Redshift Spectrum can do much more than Athena and it can be scaled up or down as needed without any downtime in between.