This blog post covers how Psyberg helps automate end-to-end catchup of pipelines, including dimension tables. It explores the state of pipelines after incorporating Psyberg and how different modes can help with multistep data pipelines. The workflow structure for both stateful and stateless processing with Psyberg is explained, detailing stages like initialization, Write-Audit-Publish process, and Psyberg Commit. The setup for automated catchup with Psyberg in a customer lifecycle example is described, showcasing features like Catchup Threshold and Metadata Recording. The blog also discusses how Psyberg transformed workflows, improving resource utilization, workflow onboarding, reliability, and accuracy. It concludes with the impact of Psyberg on data processing workflows and future steps towards enhancing data platform capabilities.