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Using Cloud Functions and Cloud Schedule to process data with Google Dataflow
- This project showcases the integration of Google Cloud services, specifically Dataflow, Cloud Functions, and Cloud Scheduler, to create a highly scalable, cost-effective, and easy-to-maintain data processing solution.
- The project uses Google Dataflow, a fully managed service for stream and batch data processing, built on Apache Beam to handle large-scale data processing tasks.
- It also demonstrates the use of Cloud Functions, a serverless execution environment that allows you to run code in response to events.
- Google Cloud Scheduler is used to automate the execution of the Cloud Functions, ensuring that Dataflow jobs run as needed without manual intervention.
- The project implements a CI/CD pipeline with GitHub Actions for automated deployments using comprehensive error handling and logging for reliable data processing.
- Before getting started, ensure you have a Google Cloud account with billing enabled and a GitHub account.
- The project requires the creation of a Google Cloud Storage bucket to store your data, a BigQuery dataset where the data will be ingested, and a Dataproc cluster for processing.
- After setting up a service account, the project requires granting Storage Access Permissions, Dataflow Permissions, Permissions to Create and Manage Cloud Functions and Cloud Scheduler, and Permissions to Manage Service Accounts.
- Ensure that the environment variables and secrets are set in the deployment configuration or within GitHub Secrets to configure bucket paths, process names and steps.
- GitHub Actions uses service account credentials to authenticate with Google Cloud and execute the necessary workflow jobs containing four steps - enable-services, deploy-buckets, build-dataflow-classic-template, deploy-cloud-function, and deploy-cloud-schedule.
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