Aws redshift spectrum full#

Using Athena opens up an opportunity to create a full serverless data analytics stack. Have data in locations other than your data lake? You can extend Athena via federated query services. One significant difference is that Spectrum requires Redshift, which must be factored into your total cost. spinning up Redshift clusters.Ī query in Athena and Spectrum generally has the same compute cost of $5 per terabyte scanned. Assuming you have objects on S3 that Athena can consume, then you might start with Athena vs. AWS Athena is based on Facebook Presto and includes some Apache Hive goodness. If you are not a Redshift customer, then it becomes more interesting. You Are NOT An Existing Redshift Customer Access to the “Redshift+Redshift Spectrum” tandem has costs that might not be worthwhile (right now) if you are NOT an AWS Redshift customer. Redshift Spectrum is not an option without Redshift. Lastly, remember that a key difference from Athena is that access to Spectrum requires an active, running Redshift instance. These new capabilities may tip the scales in favor of sticking with Redshift. Redshift federated queries were released in 2020. Any connector you have loading data to Redshift will need to make sure it supports the file format and workflows associated with Spectrum Not a big deal, but make sure any ETL or ELT data processing for use within Spectrum should account for external tables. Keep in mind that Spectrum data resides in an external schema. This includes any ancillary data operations that process data into a Redshift warehouse or unique and complex data types supported by Redshift that may break existing workflows. Quick Pick? Stick With Redshift vs AthenaĪs an existing Redshift user, I would be less inclined to use Athena because of existing investments in Redshift. One caveat is that you still have constraints for concurrent users in a Redshift for various analytical workloads compared to Athena. Also, complex analytic queries will work equally well in either system and will have the same cost profile for compute resources. The benefit of this approach is offloading data so you can be more efficient with local storage in Redshift. In both cases, you pay for each terabyte of data scanned.

Note: You are still paying “per query” for the amount of data scanned via Spectrum the same as Athena. Why pay to store that data in Redshift, adjusting cluster size to hold more data when moving it to external tables on AWS S3 and query data with Spectrum is an option? This approach can minimize the need to scale Redshift requires a new node for improving consistent performance for both a simple or complex query, which can be expensive! This can save you big dollars since you can get lifecycle data out of Redshift to S3.įor example, let’s say you have a 100 GB transactional table of infrequently accessed data within one of your Redshift Operational Databases. Spectrum allows you to extend beyond typical data warehousing and dense storage by directly querying a data lake. If you are already a Redshift customer, Amazon Redshift Spectrum can help you balance the need for adding capacity to the system. Here are four questions you can ask yourself to help frame which may work best for your situation: 1. Rather than try to decipher technical differences, the post frames the choice as a buying, or value, question. However, most of the discussion focuses on the technical difference between these Amazon Web Services resources.Īthena & Redshift Spectrum are excellent choices for their respective use cases. This question about interactive query services AWS Athena and Redshift Spectrum database has come up a few times in various posts and forums. Which data lake SQL query engine? Redshift Spectrum vs.

Aws redshift spectrum