Amazon Redshift Spectrum
The course is part of this learning path
This course covers Amazon Redshift Spectrum, including what it is, what it does, how it works, and some points to take into consideration when using Redshift Spectrum.
- How to manage cold data in Redshift using Amazon S3
- What Amazon Redshift Spectrum is and does
- How Spectrum Queries work
- Supported data formats of Spectrum
- File optimization using Spectrum
- Amazon Redshift Spectrum Considerations
This course is intended for people that want to learn more about Amazon Redshift Spectrum and how it can be used to perform SQL queries on data stored in Amazon S3.
To get the most from this course, you should have a basic understanding of Amazon Redshift, Amazon Athena, AWS Glue, and data analytics concepts.
Redshift Spectrum Cost Considerations. Charges for Spectrum are calculated based on the number of bytes scanned rounded up to the next megabyte. There is a 10-megabyte minimum per query. There are no charges for Data Definition Language, DDL, statements. So you can run as many CREATE, ALTER, and DROP TABLE statements as needed. In general, and I've mentioned this in other ways, query performance can be improved, and costs can be reduced, by storing data in a compressed, partitioned, and columnar format.
If data is compressed using one of Spectrum's supported formats, cost go down because less data is scanned. Similarly, if data is stored in a columnar format such as Apache Parquet or Apache ORC, there will be fewer changes because Spectrum only scans columns needed by the query. As a reminder, Spectrum is part of Amazon Redshift, so you will be charged for any Redshift clusters provisioned. Data stored in S3 also include charges based on the amount of data stored as well as the number of requests made against S3 buckets. If you use the AWS Glue Data Catalog with Redshift Spectrum, you will be charged standard Glue Data Catalog rates. Finally, if you use KMS to encrypt data inside S3 buckets, you will be charged standard AWS KMS rates.
Stephen is the AWS Certification Specialist at Cloud Academy. His content focuses heavily on topics related to certification on Amazon Web Services technologies. He loves teaching and believes that there are no shortcuts to certification but it is possible to find the right path and course of study.
Stephen has worked in IT for over 25 years in roles ranging from tech support to systems engineering. At one point, he taught computer network technology at a community college in Washington state.
Before coming to Cloud Academy, Stephen worked as a trainer and curriculum developer at AWS and brings a wealth of knowledge and experience in cloud technologies.
In his spare time, Stephen enjoys reading, sudoku, gaming, and modern square dancing.