Amazon Redshift Workload Management Powered By Machine Learning

Amazon Web Services (AWS), claims that its cloud data warehouse, Amazon Redshift is used to power mission-critical analytical workloads for “Forty 500 companies, startups, or everything in between.” Machine learning now powers the automatic management of these workloads.
Redshift’s recently announced Automatic workload management, (WLM), for Redshift allows dynamically managing memory and query concurrency to increase query throughput.
AWS stated that users can enable concurrency scaling to increase the number of concurrent queries in a query queue to virtually unlimited numbers. They can also prioritize important questions.
AWS stated last week that setting query priorities allows you to ensure that higher priority workloads receive preferential treatment in Redshift, including more resources during busy times for consistent query performances. “Automatic WLM uses intelligent algorithms so that lower priority queries don’t stall but still make progress. See Query Priority for more information.
The cloud giant advises customers who manage their workloads manually to switch to Automatic WLM. The “Implementing Automatic WLM” guide provides guidance.