Apache Airflow Overview | Architecture | What is DAG | Tasks | Operators | Use Cases

[Music] hello data Pros Welcome to our new series on aache airflow in this short video we'll talk about what airflow is see why it's so popular understand the basic concepts such as dags tasks and operators break down its architecture and core components and finally explore the use cases where airflow excels let's begin right away Apache airflow is an open source platform that empowers data professionals to efficiently create schedule and monitor tasks and workflows you may be wondering why airflow is gaining so much popularity well there are a few key reasons airflow uses python a language already familiar to data professionals it offers many plug andplay operators which allows you to Define workflows that span across cloud services like AWS Azure gcp snowflake data bricks and even on premises servers with seamless integration with cloud computing resources and kubernetes services airflow is designed for Limitless scalability it comes with a rich web user interface that makes it easy to visualize Monitor and troubleshoot your workflows airflow is open source and cost effective making it the ideal choice for many organizations with Millions of users and thousands of contributors it's on a path of continuous growth when working with airf flow you'll frequently encounter the term dag which stands for directed acyclic graph dag is a fundamental Concept in Apache air flow and serves as a representation of a workflow directed refers to the fact that dependencies between the tasks have a specified Direction a cyclic means there are no Cycles so you cannot execute one task and later return back to the same task a graph is a diagram that consists of nodes and edges where nodes represent tasks and the edges represent dependencies here is a sample airflow dag as mentioned earlier dags are written in Python dag consists of one or more tasks each task is created by using an operator an operator is an abstraction that defines what needs to be done in a task furthermore the parameters defined within the operator enable you to customize the task according to your requirements tasks are connected by dependencies indicating the order in which they should be executed dag can also have a schedule which specifies when the dag should be executed let's explore how this dag appears in the airflow user interface everything we've defined programmatically in Python is now visible in the UI as expected let's move further and understand the core components in the airflow architecture web server serves the web user interface that allows users to Monitor and manage workflows database is used to store metadata about workflows tasks and their dependencies it also stores the status of your workflows and tasks such as running failed or succeeded the scheduler is responsible for scheduling your workflows to run it frequently pulls the database for new or modified dags and accordingly sends them to executor executor is responsible for receiving tasks from the scheduler assigning them to workers and monitoring their execution workers are the compute layer that runs the tasks they are responsible for fetching tasks from the executive completing them as defined and finally reporting their status back to the executive based on the specified operator and parameters workers complete the desired task by interacting with respective data platforms and services let's explore a real world use case of airflow airflow can be used for nearly any batch data processing workflow its extensive library of operators and exceptional extensibility make it particularly powerful in orchestrating workflows spanning multiple systems with complex dependencies the diagram that you see illustrates a sample use case that can be achieved with airflow despite involving a diverse set of tools and Technologies airflow can serve as a unified workflow management solution for both orchestration and monitoring that's all for today please stay tuned for our upcoming videos where we'll demonstrate how to set up airflow on your local machine to Kickstart our airflow development Journey please do like the video and subscribe to our Channel if you have any questions or thoughts please feel free to leave them in the comments ments section below thanks for watching

Channel: Sleek Data

Views: 23,456

Keywords: airflow, apache, architecture, components, cases, operators, dag, aws, core, github, task, example, difference, between, installing, windows, pipelines, install, tutorial, tasks, apache airflow, apache airflow tutorial, Airflow architecture, apache airflow use cases

Id: s6PgXq-SO4I

Length: 5min 4sec (304 seconds)

Published: Sat Sep 16 2023