Data processing in information systems is divided into three stages: extraction, transformation, and loading (Extract Transform Load, ETL). In solutions using Big Data, it is with the help of ETL that the original (“raw”) data is converted into information suitable for business analysis.
However, as data grows and analytical tasks become more complex, the number of ETL processes that must be planned, monitored, and restarted in the event of failures also increases – the need for an orchestrator arises.
In this article, we will talk about an effective open-source Apache Airflow tool that helps manage complex ETL processes and works well with the principles of Cloud-Native applications.
Data processing processes, or pipelines, in Airflow are described below. This is a semantic combination of tasks that must be performed in a strictly defined sequence according to a specified schedule. Visually, the DAG looks like a directed acyclic graph, a graph that does not have cyclic dependencies.
DAG nodes perform tasks. These are direct operations applied to data, for example, loading data from various sources, aggregating them, indexing, clearing duplicates, saving the obtained results, and other ETL processes. At the code level, tasks can be Python functions or Bash scripts.
Operators are most often responsible for the implementation of tasks. If tasks describe what actions to perform with data, then operators describe how to perform these actions. It is a template for completing tasks.
A special group of operators is made up of sensors (Sensors), which allow prescribing a reaction to a specific event. The trigger can be the arrival of a specific time, the receipt of a certain file or line with data, another DAG / Task, and so on.
AirFlow has a rich selection of built-in operators. In addition, many custom operators are available by installing community-supported vendor packages. It is also possible to add custom operators by extending the BaseOperator base class. When frequently used code based on standard operators appears in your project. It is recommended that you convert it to your own operator.
The AirFlow architecture is based on the following components:
Also Read: What Is Apache Spark, And How Is It Used In Big Data
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