A DAG is a collection of tasks with defined dependencies, allowing users to visualize and understand the workflow structure easily. Directed Acyclic Graph (DAG) Based: Airflow uses Directed Acyclic Graphs (DAGs) to represent workflows.Some of the key features of Apache Airflow include:
It provides several powerful features that make it a popular choice for managing and scheduling workflows in various data engineering and data science tasks. What are the capabilities of Apache AirflowĪpache Airflow is an open-source platform used for orchestrating complex workflows and data processing pipelines. The code for this article can be found here. Each stage, from gathering data to storing the features in the feature store, is carried out as a task within the Airflow DAG.
#Airflow dag naming convention how to
We will also explore how to seamlessly integrate the resulting features into a feature store, a centralized repository for storing and serving feature data. Aggregations, on the other hand, help summarize and condense data by grouping and computing statistical measures.īy leveraging Airflow's capabilities, we can orchestrate and automate the feature engineering process, ensuring reproducibility and consistency.
Feature binning allows us to discretize continuous features into bins, enabling better modeling of non-linear relationships. We will delve into building a feature pipeline using Airflow, focusing on two tasks: feature binning and aggregations. In this article, we explore the power of Apache Airflow, a popular open-source platform for workflow orchestration, in the context of feature engineering. However, performing feature engineering efficiently and at scale can be challenging, especially when dealing with large datasets and complex transformations. It involves transforming raw data into meaningful features that capture the underlying patterns and relationships in the data. Feature engineering is a critical aspect of building robust and accurate machine learning models.
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