Understanding Amazon Redshift Spectrum: A Brief Overview

Amazon Redshift Spectrum

Introduction to Amazon Redshift

Amazon Redshift is Amazon’s Data Warehouse offering that forms a crucial part of Amazon’s Cloud Computing Platform, Amazon Web Services (AWS). Amazon Redshift ensures that you can query and combine exabytes of Semi-Structured and Structured data across your Operational Database, Data Warehouse, and Data Lake using standard SQL. You can also compare the Snowflake vs Redshift Data warehouse.

It also reportedly performs 3x better than its competitor Cloud Data Warehouses based on price performance. It takes advantage of Machine Learning and AWS-designed hardware to deliver the optimal price-performance at any scale.   

Introduction to Amazon Redshift Spectrum

The Amazon Redshift Spectrum is a handy tool that allows you to retrieve and query Semistructured and Structured data from files in Amazon S3. It can do this without the need to load the data into the Amazon Redshift tables.

Amazon Redshift Spectrum inhabits dedicated Amazon Redshift servers that operate independently of your clusters. Amazon Redshift Spectrum pushes a lot of compute-intensive tasks like aggregation and predicate filtering, down to the Amazon Redshift Spectrum layer. Therefore these queries require and leverage comparatively lesser processing capacity of your cluster.

Amazon Redshift queries employ extensive parallelism to execute immensely fast against large datasets. The majority of the processing takes place in the Amazon Redshift Spectrum layer, while most of the data remain in Amazon S3.

Understanding the Considerations for Amazon Redshift Spectrum

Here are a few considerations to keep in mind when leveraging Amazon Redshift Spectrum:

  • If your Amazon Redshift cluster uses Enhanced VPC Routing, you might need to perform additional configuration steps.
  • The Amazon Redshift cluster and the Amazon S3 bucket must be in the same AWS region.
  • You cannot perform delete or update operations on external tables. You can use CREATE EXTERNAL TABLE command to create a new external table using the specified schema.
  • You cannot control user permissions on an external table unless you are using an AWS Glue Data Catalog enabled for AWS Lake formation. However, you can grant and revoke permissions on the external schema. 
  • If you are using AWS Glue Data Catalog or the Athena Data Catalog as a metadata store, take a look at Quotas and Limits in the Amazon Redshift Cluster Management Guide. 
  • To run Amazon Redshift Spectrum-based queries, the database user must have permission to create temporary tables in the database. For instance, the following query grants temporary permission on the database “spectrumdb” to the “spectrumusers” user group:

grant temp on database spectrumdb to group spectrumusers;

Conclusion

This blog talks about the basics of Amazon Redshift Spectrum and the things you should consider before leveraging it to query external data.

Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be challenging, and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases, SaaS applications, etc., to Data Warehouses like Amazon Redshift to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code. You can try Hevo for free by signing up for a 14-day free trial. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

LEAVE A REPLY

Please enter your comment!
Please enter your name here