What is Airflow
Airflow is an open-source tool for data pipeline management. It helps you to organize your pieces of code into DAGs, run them, and test and monitor the execution.
Advantages
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Based on Python
Python is used to describe ETL/ELT processes. And anyone with knowledge of Python will find using Airflow easy.
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A Small but Full-Fledged Toolkit
Great for creating and managing data processing processes. Working with AirFlow is possible using CLI, REST API and a web interface created on the basis of the Flask Python framework.
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Integration
AirFlow supports many databases (MySQL, PostgreSQL, DynamoDB, Hive), big data storage (HDFS, Amazon S3), and cloud platforms (Google Cloud platform, Amazon Web Services, Microsoft Azure).
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An Extensible REST API
Makes it relatively easy to integrate Airflow into an existing enterprise IT landscape and flexibly customize data pipelines.
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Monitoring and Alerting
Integration with Statsd and FluentD is supported for collecting and sending metrics and logs.
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Role-Based Access
AirFlow provides 5 roles with different access levels: Admin, Public, Viewer, Op, User. Integration with Active Directory and flexible access configuration using RBAC are possible.
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Testing Support
It’s possible to use basic unit tests to test pipelines and specific tasks in them.
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Scalability
Airflow is scalable due to its modular architecture and message queue for an unlimited number of DAGs
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Open Source
AirFlow is actively maintained by the community and has well documented documentation.
Analytics Data Stack Structure
Airflow Alternatives
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Luigi
Luigi is a Python framework for building complex sequences of dependent tasks. A fairly large part of the framework is aimed at transforming data from various sources (MySQL, MongoDB, Redis) and using various tools (from starting a process to executing tasks of various types on a Hadoop cluster).
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Dagster
Dagster is an orchestrator that’s designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.
