Learn DAGY by Example
A progressive collection of runnable examples covering every core concept; from a minimal hello-world to production ETL pipelines and cloud deployment.
Browse by Category
Basics
Core building blocks: tasks, flows, parameters, return values, and logging.
Hello World
The minimal DAGY example; define a task, wrap it in a flow, and deploy.
Linear Chain
A sequential A → B → C → D pipeline that passes data through each stage.
Parameters
Typed flow parameters with defaults and validation.
Return Values
Rich return types (lists, dicts) passed through a pipeline chain.
Logging
Capture print() and Python logging output as structured task events.
Artifacts
Persist files (text, JSON) alongside your pipeline runs using LocalArtifact.
Graph Patterns
DAG topology patterns: fan-out, fan-in, and diamond shapes.
Error Handling
Retry strategies, timeouts, conditional retries, and fail-fast behaviour.
Simple Retry
Fixed-delay retries for transient failures.
Exponential Backoff
Retry with exponentially increasing delays between attempts.
Jitter
Add randomised jitter to retry delays to avoid thundering herd.
Timeout
Task-level timeout enforcement to prevent runaway tasks.
Retry Condition
Selective retry based on exception type using a condition function.
Fail Fast
Cancel pending tasks as soon as one task fails.
Hooks
Lifecycle callbacks at the task and flow level for observability and auditing.
Concurrency
Parallel execution with worker pools and per-task concurrency limits.
Real World
End-to-end production patterns: ETL, validation, and report generation.
Deployment
Build artifacts, deploy to DAGY Cloud, and trigger remote runs.