Automation of routine data engineering processes is an essential element for any business system. It allows IT teams to perform simple tasks quickly and accurately. It enables engineers to focus on solving new technical business challenges rather than performing typical tasks periodically. Airflow is often used to automate processes.
Apache Airflow: Advanced Workflow Manager
Airflow is a workflow management platform. This tool helps create programs, each of which performs one specific task.
Further, graphs are compiled from these programs. The graph specifies the sequence of execution of the programs and the conditions under which they must be fulfilled. To automate workflow execution, developers should create a description of the workflow as a graph.
Then it can be launched both on a schedule and at a user's command.
Airflow allows developers to reuse the code: one program can be applied to solve subtasks of several complex tasks simultaneously.
A typical scenario for using Airflow is to create new graphs from already made operators. Airflow is guided by the principle of "Configuration as code".
This means that the graph is set by writing Python code. On the one hand, this gives very great flexibility. On the other hand, this approach greatly complicates the creation of graphs. In this case, most graphs look very similar and run with the same settings: their code can be generated automatically by a set of operators and relations between them. The user interface is used to view what DAGs are doing, run DAGs, view logs, perform limited debugging, and solve problems with DAGs.
Advantages for Data Engineering
One of the reasons Apache Airflow has become so popular is that it provides data engineers with a powerful unified engine that is fast and easy to use. It allows software developers to manage things, such as machine learning tasks, graph computation, streaming, and real-time execution of multiple queries on a much larger scale.
Airflow's main big data advantages are:
• a small but complete tool for creating data processing and management processes;
• a graphical web interface for creating data pipelines that provides a relatively low entry threshold into the technology, allowing to work with Airflow not only for data engineers but also for the analysts, developers, administrators, and DevOps engineers. The user can track the life cycle of data in chains of related tasks presented in the form of a Directed Acyclic Graph (DAG);
• an extensible REST API that is relatively easy to integrate Airflow into the IT landscape of enterprise infrastructure and flexibly configure data pipelines, such as transferring POST parameters to DAG;
• integration with many sources and services - databases (MySQL, PostgreSQL, Amazon Redshift, Hive), Big Data storage (HDFS, Amazon S3), and cloud platforms (Google Cloud Platform, Amazon Web Services, Microsoft Azure);
• availability of an own metadata repository based on the SqlAlchemy library, which stores task states, DAGs, global variables, etc.;
• scalability with modular architecture and message queuing for unlimited DAGs.