Unlock the advanced functions of your cloud-based MPP data warehouse.
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