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.

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Advantages

  • Based on Python

    Python is used to describe ETL/ELT processes. And anyone with knowledge of Python will find using Airflow easy.

  • 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.

  • 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).

  • An Extensible REST API

    Makes it relatively easy to integrate Airflow into an existing enterprise IT landscape and flexibly customize data pipelines.

  • Monitoring and Alerting

    Integration with Statsd and FluentD is supported for collecting and sending metrics and logs.

  • 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.

  • Testing Support

    It’s possible to use basic unit tests to test pipelines and specific tasks in them.

  • Scalability

    Airflow is scalable due to its modular architecture and message queue for an unlimited number of DAGs

  • Open Source

    AirFlow is actively maintained by the community and has well documented documentation.

Analytics Data Stack Structure

Data Ingestion
Server Technologies
Databases
Data Transformation
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Airflow Alternatives

  • 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).

  • Dagster

    Dagster is an orchestrator that’s designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.

How We Use Airflow

  • Twinero

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  • Refocus

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