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apache airflow

Apache Airflow: Monitor and manage the data pipelines and complex workflows
Ali Mohsin
| December 3, 2022

Data Science Dojo is offering Apache Airflow for FREE on Azure Marketplace packaged with a pre-configured web environment of Airflow with various data analytics features.  

  

Introduction:  

In this era of tighter data restrictions, it is more important than ever to understand, analyze, and manage your data throughout its lifecycle. It is harder than ever as data volumes rise, and data pipelines get more complicated. A solution is needed Organizations or Individuals must have a complete, scalable, easy-to-analyze platform to manage and monitor the complex workflows and support several integrations. 

 

What is Apache Airflow?  

Apache Airflow, a powerful open-source tool for authoring, scheduling, and monitoring data and computational workflows. It provides a method that makes it easier to manage, schedule, and coordinate complicated data pipelines from several sources. 

 

What is DAG? 

A DAG, or Directed Acyclic Graph, in Airflow is a list of all the jobs you wish to execute, arranged to reflect their connections and dependencies. A Python script that expresses the DAG’s structure as code defines a DAG. Researchers’ priori ideas about the connections between and among variables in causal structures are encoded using DAGs. It contains directed edges (arrows), linking nodes (variables), and their paths. Hence A workflow is represented as a DAG, which consists of discrete units of work called Tasks that are ordered considering relationships and data flows. 

 

Apache Airflow Architecture: 

This powerful and scalable workflow scheduling software is made up of four key parts: 

  • Scheduler: The scheduler keeps track of all DAGs and the jobs they are connected to. To start, it frequently checks the list of open tasks. 
  • Web server: The user interface for Airflow is the web server (The default port Apache Airflow listens to is 8080). It displays the status of the jobs, gives the user access to the databases, and lets them read log files from other remote file stores like Microsoft Azure blobs. 
  • Database: To make sure the schedule retains metadata information, the state of the DAGs and the tasks they are connected to, are saved in the database. The scheduler scans each DAG and records essential data, including schedule intervals, run-by-run statistics, and task instances. 
  • Executors: There are various kinds of executors for different use cases. Few examples of Executors are  SequentialExecutor, LocalExecutor, CeleryExecutor, and KubernetesExecutor 

  

(With SequentialExecutor, just one task may be carried out at once. No parallel processing is possible. It is useful when testing or debugging. LocalExecutor supports hyperthreading and parallelism. It is excellent for using Airflow on a single node or a local workstation. CeleryExecutor is usually used for managing a distributed Airflow cluster. While using the Kubernetes API, the KubernetesExecutor creates temporary pods for each of the task instances to run in.) 

 

Key features Apache Airflow provides: 

  • Dynamic Pipelines can be constructed by Airflow dynamic, also as it is constructed in the form of code which gives an edge to dynamic behavior. 
  • Apache Airflow has a rich User Interface that helps the user to manage their workflow easily 
  • It gives a separate code view pallet that enables users to view their DAGs code as well.  
  • Allows users to visualize their DAGs in different forms like Gantt chart, Tree, and Graph. 
  • With ready to use operators in airflow, users can work with various cloud platforms like Microsoft Azure, AWS (Amazon Web Services) etc. 
  • Allows role-based user management to maintain Security and Accessibility.

 

Apache Airflow with Azure services: 

Apache Airflow leverages the power of Azure services to make the procedure of monitoring and managing complex workflows intuitively. Also with Azure, Airflow made it a more scalable data warehousing platform. Airflow enables users to work in a scalable environment. 

 

Conclusion:  

Other open-source Data Engineering solutions put intense competition on Apache Airflow. But it is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. Users can easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status all in a single package.  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We therefore know the importance of data and the encapsulated insights. Through this offer, we are confident that you can analyze, visualize, and query your data in a collaborative environment with greater ease. 

Install the Apache Airflow offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Apache Airflow for FREE by clicking on “Try now.”  

 

CTA - Try now

 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

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