Arenadata Orchestrator

The flexible capabilities of the scheduler, combined with its reliability, fault tolerance and scalability, make the platform indispensable for planning and orchestrating processes of any complexity.

Machine learning applications require careful data organization to manage the entire lifecycle of models – from their creation to deployment and monitoring.

ADO is a powerful data management platform built on Apache Airflow. It provides robust tools to streamline MLOps workflows by simplifying model development, deployment, and maintenance.

Data pipelines written in Python make it easy to turn custom functions into tasks and interact with any API, making it a great tool for managing your infrastructure, such as Kubernetes clusters.

ADO has built-in integration with a variety of data and analytics platforms and tools, and supports all the popular Python libraries used by data scientists, data engineers, and other professionals. This provides teams with an easy-to-configure orchestration framework that can be integrated into their preferred tools. In this way, ADO makes it easy to collaborate on designing, debugging, and maintaining data pipelines as code, accelerating the development process and making deployment and maintenance easier.

Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) data pipelines are the most common use cases for Apache Airflow due to the following features: