![]() When referencing the tables in Redshift, it would be read by Spectrum (since the data is on S3). We uploaded the data to S3 and then created external tables using the Glue Data Catalog. The second dataset is user clicks on ads – this data contains 20.2 thousand rows. The first dataset is ad impressions (instances in which users saw ads) and contains 2.3 million rows. We used two online advertising data sets. We will then compare the results when it comes to query performance and costs. #REDSHIFT SPECTRUM PARQUET SERIES#In this article, we will attempt to quantify the impact of S3 storage optimization on Redshift Spectrum by running a series of queries against the same dataset in several formats – raw JSON, Apache Parquet, and pre-aggregated data. However, as we’ve covered in our guide to data lake best practices, storage optimization on S3 can dramatically impact performance when reading data. ![]() ![]() Redshift Spectrum was introduced in 2017 and has since then garnered much interest from companies that have data on S3, and which they want to analyze in Redshift while leveraging Spectrum’s serverless capabilities (saving the need to physically load the data into a Redshift instance). Amazon Redshift Spectrum is a feature within the Amazon Redshift data warehousing service that enables Redshift users to run SQL queries on data stored in Amazon S3 buckets, and join the results of these queries with tables in Redshift. ![]()
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