Apache Airflow is an open-source platform designed to automate, schedule, and monitor complex workflows or data pipelines. It allows users to create, manage, and execute workflows using a Python-based programming language. Airflow provides a graphical interface that enables users to define and visualize their workflows as Directed Acyclic Graphs (DAGs), which represent the relationships and dependencies between tasks.
With Apache Airflow, users can schedule and run tasks at specific time intervals, monitor and track the progress of workflows, handle task dependencies and retries, and manage the execution of operations such as data extraction, transformation, and loading (ETL) processes. It also supports various integrations with external systems and tools, allowing seamless interaction with databases, cloud providers, and other data processing platforms.
Apache Airflow has gained popularity due to its flexibility, scalability, and rich set of features. It is widely used in data engineering, data science, and DevOps workflows for orchestrating and automating complex data pipelines and workflows.
Orchestration systems decide where and when workloads run on a cluster of machines (physical or virtual). On top of that, orchestration systems usually help manage the lifecycle of the workloads running on them. Nowadays, these systems are usually used to orchestrate containers, with the most popular one being Kubernetes.
There are many advantages to using Orchestration tools: