Data Processing in GCP With Apache Airflow and BigQuery

This post was originally published on DZone (IoT)

In today’s data-driven world, efficient data processing is paramount for organizations seeking insights and making informed decisions. Google Cloud Platform (GCP) offers powerful tools such as Apache Airflow and BigQuery for streamlining data processing workflows. In this guide, we’ll explore how to leverage these tools to create robust and scalable data pipelines.

Setting up Apache Airflow on Google Cloud Platform

Apache Airflow, an open-source platform, orchestrates intricate workflows. It allows developers to define, schedule, and monitor workflows using Directed Acyclic Graphs (DAGs), providing flexibility and scalability for data processing tasks. Setting up Airflow on GCP is straightforward using managed services like Cloud Composer. Follow these steps to get started:

Create a Google Cloud Composer environment: Navigate to the Cloud Composer section in the GCP Console and create a new environment. Choose the desired configuration options, such as the number of nodes and machine type. Install additional Python packages: Airflow supports custom Python packages for extending its functionality. You can install additional packages using the requirements.txt file or by directly installing them from within Airflow’s web interface. Configure connections: Airflow uses connection objects to connect to external systems like BigQuery. Configure the necessary connections in Airflow’s web interface by providing credentials

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