Netflix transitioned from using the Meson orchestrator to Maestro due to scalability issues with the growing volume of data and workflows. Maestro, built with a distributed microservices architecture, efficiently manages large-scale workflows with high reliability and low operational overhead. It supports dynamic workflows, defined via DSLs, a visual UI, or programmatic APIs, and leverages technologies such as CockroachDB and distributed queues. Features like event publishing, parameterized workflows, and an integrated signal service enable Maestro to handle extensive data processing and machine learning tasks at scale.
Table of contents
WorkOS + MCP: Authentication for AI Agents (Sponsored)AI-assisted coding is faster. But is it safe? (Sponsored)Maestro ArchitectureScalability Techniques for MaestroMaestro Execution AbstractionsMaestro DSL InterfaceAggregated View and RollupEvent PublishingConclusionSPONSOR USSort: