How Netflix’s Open‑Source Maestro Powers Scalable Data & ML Workflows
Netflix has open‑sourced its Maestro workflow orchestrator, a highly scalable, DAG‑based system built on Git, Java, Gradle and Docker that handles millions of daily jobs for data scientists, enabling ETL, ML pipelines, A/B testing and more, while meeting strict SLOs.
Netflix has released its Maestro workflow orchestrator as an open‑source project under the Apache 2.0 license, providing data scientists and business managers with a service‑level‑goal‑aware platform that can run up to two million jobs per day.
Technical Principles of Maestro
According to Netflix engineers, Maestro is highly scalable and can meet strict SLOs even during traffic peaks.
It is built on a stack of open‑source technologies including Git, Java 21, Gradle and Docker.
Maestro can be invoked via cURL; workflows are defined in JSON and the business logic can be packaged as Docker images, Jupyter notebooks, Bash scripts, SQL, Python, or other formats.
Behind the scenes Maestro manages the entire lifecycle of a workflow, handling retries, queuing and task assignment to compute engines. It supports directed acyclic graphs (DAGs), as well as looping workflows, sub‑workflows and conditional branches.
Engineers note that Maestro supports a wide range of use cases such as ETL pipelines, machine‑learning workflows, A/B‑test pipelines, and data‑movement pipelines between storage systems, while its horizontal scalability allows it to manage both massive numbers of workflows and large numbers of jobs within a single workflow.
Netflix has a long history of open‑sourcing internal tools; previous projects include Chaos Monkey, Zuul, and the now‑retired Conductor.
The original orchestrator, named Meson, handled thousands of jobs per day but required vertical scaling and approached AWS instance limits. Anticipating at least a 100 % annual growth in workload, Netflix redesigned the system as Maestro to be highly extensible and stateless, with each workflow composed of independent steps that can be scaled according to business needs.
Developer‑friendly open‑source workflow engine
Scalable DAG architecture with support for loops and conditionals
Integration with Docker, Jupyter, and multiple scripting languages
Designed for high‑throughput data‑science and ML pipelines
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