Best Practices for Scaling Amazon Redshift

Level: 300

As data grows in volume, analytical needs grow with it. Complex analytical workloads and unplanned periods of high analytical demand cause sluggish performance, while data admins and engineers struggle to scale their data warehouse and data lake infrastructure quickly. In this tech talk, AWS Solution Architect Tony Gibbs will talk about the most common customer pain points he hears on a day-to-day basis and how customers using Amazon Redshift are able to quickly scale up to match their analytical needs and data volume, and then scale back down when demand subsides to save cost.