Supercharging Web UIs with Druid Databases Preparing and ingesting Druid databases from Delta Lake tables on Amazon’s S3 cloud – the nuances you need to know.
Accurately measuring lag in a Spark streaming data pipeline to DeltaLake How to properly use the Spark StreamingQueryListener to capture component lag with Prometheus
How ZipRecruiter uses instance type prioritization and automatic node-group scaling to save money and avoid massive reclamations
If you’ve applied for a job lately, it’s all but guaranteed that your application was reviewed by software—in most cases, before a human ever laid eyes on it. In this episode, the first in a four-part investigation into automated hiring practices, we speak with the CEOs of ZipRecruiter and Career Builder, and one of the architects of LinkedIn’s algorithmic job-matching system, to explore how AI is increasingly playing matchmaker between job searchers and employers. But while software helps speed up the process of sifting through the job market, algorithms have a history of biasing the opportunities they present to people by gender, race...and in at least one case, whether you played lacrosse in high school.
ZipRecruiter has built one of the world’s largest, online, double-sided job marketplaces. It enables job-seekers to find their next great career opportunity, and helps employers discover the best available candidates for their open positions. A key feature of our marketplace is making ‘the search’ as efficient as possible.
ZipRecruiter’s migration from a monolith to Kubernetes microservices increased reliability and deployment speed. But that was just the beginning.