Introduction
Description
Workflow orchestration is what turns “a script that works” into a pipeline you can actually trust in production. In this course, you’ll learn how to build scalable, reliable workflows with Kestra on Google Cloud Platform (GCP) — from local development all the way to event-driven execution in the cloud.
This is a fully hands-on course. You’ll set up Kestra locally with Docker, connect it to GCP, and build real workflows that run Python and Java, integrate with GitHub, and orchestrate scalable data pipelines using Cloud Storage (GCS) + BigQuery.
Along the way, you’ll learn how to design maintainable workflows with subflows, parallel execution, error handling, retries, monitoring, dashboards, and event-driven triggers without turning your orchestration layer into a messy processing framework.
By the end of this course, you’ll be able to build modular, production-ready pipelines that scale cleanly and run reliably.
Why Kestra?
Kestra is a modern orchestration platform built around a simple idea: workflows should be easy to define, easy to run, and easy to scale. You define flows in YAML, connect tasks through inputs and outputs, and rely on Kestra’s execution engine for scheduling, retries, observability, and real operational control.
It works locally, on your own infrastructure, or in the cloud, and it integrates deeply with platforms like GCP, making it a great choice for engineers who want orchestration flexibility without limits.
What You’ll Build in This Course
The Kestra Mental Model (Workflows done right)
You’ll start by building a clear mental model for Kestra and workflow orchestration. You’ll learn how flows are structured, how tasks exchange inputs and outputs, and why YAML in Kestra is more than configuration. It becomes the place where orchestration logic, conditions, loops, parameters, and SQL-based transformations come together.
Local Setup with GCP Integration
Next, you’ll set up Kestra locally using Docker and connect it to Google Cloud. You’ll configure service accounts and permissions, validate your setup end-to-end, and make sure your workflows can interact with GCP services like Cloud Storage right from the start.
Running Real Code in Your Workflows (Python + Java)
Once your environment is ready, you’ll run Python and Java as part of your workflows, from simple scripts to more structured setups. You’ll also learn how to keep code maintainable using Namespace Files instead of embedding everything directly into YAML, and how to pass data and artifacts between tasks cleanly.
GitHub Sync and Reusable Workflow Design
As workflows grow, structure becomes everything. You’ll connect Kestra to GitHub using native sync tasks so your workflows and code live in proper version control. On top of that, you’ll learn how to design modular pipelines using subflows so you can reuse logic instead of copy-pasting it across projects.
Scalable Pipelines with BigQuery + Cloud Storage
From there, you’ll build real GCP-based data workflows. You’ll load CSV data from Cloud Storage into BigQuery, build raw and clean dataset layers, run SQL transformations, and design pipelines that handle bad records properly instead of silently dropping them.
Scaling Execution: Loops, Parallelism, and Resilience
You’ll then take your workflows from “working” to production-ready. You’ll process multiple files dynamically, run independent steps in parallel, and implement error handling strategies that include retries, fallbacks, and clean isolation of failed inputs. This way, your pipelines recover safely without manual intervention.
Event-Driven Orchestration, Monitoring, and Metrics
Finally, you’ll build event-driven pipelines that trigger automatically when new files arrive in cloud storage using Pub/Sub. To round things off, you’ll learn how to monitor and debug workflows effectively, replay failed executions, and build dashboards that track workflow activity and success rates over time.
Who This Course is For
This course is for Data Engineers and Analytics Engineers who want to:
- build production-ready orchestration workflows
- scale pipelines using cloud services instead of local processing
- run code and SQL in real workflow execution contexts
- implement event-driven pipelines on GCP
- improve reliability with retries, monitoring, and structured failure handling
- move from “working workflows” to maintainable pipeline architecture
No prior Kestra experience is required. You’ll learn everything step by step while building real workflows.
Start Now:
Building Advanced Pipelines with Kestra on GCP is included in our Data Engineering Academy and also available as Free Lab
