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DE Zoomcamp Notes
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  • Welcome - Data Engineering Zoomcamp 2025 Notes
  • INTRODUCTION
    • Introduction & Set Up
      • Virtual Environments
  • MODULE 1
    • Introduction to Module 1
    • 1.1 - Google Cloud Platform GCP
      • 1.1.1 - Introduction to Google Cloud Platform
    • 1.2 - Docker & Docker-compose
      • 1.2.1 - Introduction to Docker
      • 1.2.2 - Ingesting NY Taxi Data to Postgres
      • 1.2.3 - Connecting pgAdmin and Postgres
      • 1.2.4 - Dockerizing the Ingestion Script
      • 1.2.5 - Running Postgres and pgAdmin with Docker-Compose
      • Docker-Compose Summary
      • 1.2.6 - SQL Refresher
      • Optional Docker Video
    • 1.3 - Setting up infrastructure on GCP with Terraform
      • 1.3.1 - Terraform Primer
      • 1.3.2 - Terraform Basics
      • 1.3.3 - Terraform Variables
    • Homework
  • Module 2
    • Introduction to Module 2
    • 2.1 - Introduction to Orchestration and Kestra
      • 2.1.1 - Workflow Orchestration Introduction
      • 2.1.2 - Learn Kestra
    • 2.2 - ETL Pipelines in Kestra: Detailed Walkthrough
      • 2.2.1 - Create an ETL Pipeline with Postgres in Kestra
      • 2.2.2 - Manage Scheduling and Backfills using Postgres in Kestra
      • 2.2.3 - Transform Data with dbt and Postgres in Kestra
    • 2.3 - ETL Pipelines in Kestra: Google Cloud Platform
      • 2.3.1 - Create an ETL Pipeline with GCS and BigQuery in Kestra
      • 2.3.2 - Manage Scheduling and Backfills using BigQuery in Kestra
      • 2.3.3 - Transform Data with dbt and BigQuery in Kestra
    • Bonus: Deploy to the Cloud
    • Homework
  • Module 3
    • Introduction to Module 3
    • 3.1 - Data Warehouse, Partitioning and Clustering
      • 3.1.1 - Data Warehouse and BigQuery
      • 3.1.2 - Partitioning and Clustering
    • 3.2 - BigQuery Internals and Best Practices
      • 3.2.1 - BigQuery Best Practices
      • 3.2.2 - Internals of Big Query
    • 3.3 - Machine Learning
      • 3.3.1 - BigQuery Machine Learning
      • 3.3.2 - BigQuery Machine Learning Deployment
    • Homework
  • Workshop
    • Workshop Week
    • Homework
  • Module 4
    • Introduction to Module 4
    • 4.1 - DBT the basics
      • 4.1.1 - Analytics Engineering Basics
      • 4.1.2 - What is dbt?
    • 4.2 - Creating your Project
      • 4.2.1 - Set Up Project
      • 4.2.2 - Start Your dbt Project BigQuery and dbt Cloud
      • 4.2.3 - Build the First dbt Models
      • 4.2.4 - Testing and Documenting the Project
    • 4.3 - Deployment & Visualizations
      • 4.3.1 - Deployment Using dbt Cloud
      • 4.3.2 - Visualising the data with Google Data Studio
    • Homework
  • Module 5
    • Introduction to Module 5
    • 5.1 - Install & Intro
      • 5.1.1 - Install
      • 5.1.2 - Intro to Batch Processing
      • 5.1.3 - Intro to Spark
    • 5.2 - Spark SQL and DataFrames
      • 5.2.1 - Spark & PySpark
      • 5.2.2 - Spark Dataframes
      • 5.2.3 - SQL with Spark
    • 5.3 - Spark Internals
      • 5.3.1 - Anatomy of a Spark Cluster
      • 5.3.2 - GroupBy in Spark
      • 5.3.3 - Joins in Spark
    • 5.4 - Running Spark in the Cloud
      • 5.4.1 - Connecting to Google Cloud Storage
      • 5.4.2 - Creating a Local Spark Cluster
      • 5.4.3 - Setting up a Dataproc Cluster
      • 5.4.4 - Connecting Spark to Big Query
    • Homework
  • Module 6
    • Introduction to Module 6
    • 6.1 - Stream Processing
      • 6.1.1 - Introduction
      • 6.1.2 - Intro to stream processing
      • 6.1.3 - What is Kafka?
      • 6.1.4 - Confluent cloud
      • 6.1.5 - Kafka producer consumer
      • 6.1.6 - Kafka configuration
    • Homework
  • Final Project
    • Final Project
    • How To!
      • 1 - Create a Google Cloud Project
      • 2 - API Key and Access Token Setup
      • 3 - Fork This Repo in Github
      • Ready to Run!
    • THE END
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  1. Module 2

Bonus: Deploy to the Cloud

Last updated Jan 22, 2025

Previous2.3.3 - Transform Data with dbt and BigQuery in KestraNextHomework

Last updated 4 months ago

Youtube Video | ~3 min

Now that we've got our ETL pipeline working both locally and in the cloud, we can deploy Kestra to the cloud so it can continue to orchestrate our ETL pipelines monthly with our configured schedules, We'll cover how you can install Kestra on Google Cloud in Production, and automatically sync and deploy your workflows from a Git repository.

Resources

Install Kestra on Google Cloud
Moving from Development to Production
Using Git in Kestra
Deploy Flows with GitHub Actions