Business Intelligence Software

Looker for AWS Redshift: Technical Overview

Provided by Looker

Category Business Intelligence Software

Type White Paper

Length 9

Publish Date February 01 2015

Date posted May 11 2015


AWS continues to set the standard for delivering infrastructure services in the cloud. Amazon Redshift extends that power with a high-performance petabyte-scale data warehousing solution for today's enormous analytic workloads.

In the post-cloud era, the traditional data warehousing model of moving “all data from everywhere” into an on-premises megaserver for advanced analytics breaks down. Data-savvy, born-of-the-web companies are looking for a more streamlined solution that can exploit the scale and economics of the cloud. This is what Looker and Amazon Redshift provide.

This technical paper examines the factors that impact the performance, scale, and cost of the Looker for Redshift solution. You'll learn:
•    Four methods for moving data into Redshift
•    How to tune your Redshift database to optimize Looker performance, including recommendations for schema design and architecture
•    How to tune your Looker model to optimize Redshift performance, including details on joins, filters, caching, and derived tables
•    Two methods for monitoring query performance and how your data models are being used

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