Stop writing DAG boilerplate by hand — get a runnable orchestration pipeline from your SQL in seconds.
Paste SQL queries with dependency comments and get a runnable Airflow, Prefect or Dagster pipeline — task graph inferred, retries wired, schedule included.
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Task graph, retries, schedule and connection stubs — the hour you spend every new pipeline, gone.
Comment your queries with what depends on what and the builder wires the task graph correctly.
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Real decisions, not generic templates. Every workflow runs on live market data so your moves are backed by what employers are asking for now.
Supports Apache Airflow, Prefect and Dagster — pick your orchestrator
Infers task dependencies from query order and inline comments
Includes retry logic, schedule definition and connection reference stubs
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In action
Task graph, retries, schedule and connection stubs — the hour you spend every new pipeline, gone.
Try SQL to DAGDAG — 5 nodes · 4 edges
raw_orders
source
stg_orders
transform
stg_customers
transform
joined
transform
Airflow DAG, Prefect flow or Dagster job — same SQL in, framework-native code out.
Supports Apache Airflow, Prefect and Dagster — pick your orchestrator
Go deeper
Comment your queries with what depends on what and the builder wires the task graph correctly.
Browse all premium toolsSalary, skills, employers and the Match Engine all read the same live posting feed — so you are not comparing a benchmark from 2022 against jobs from this week.
fct_revenue
sink
Mermaid
graph LR raw_orders([raw_orders]) --> stg_orders stg_orders --> joined stg_customers --> joined joined --> fct_revenue[(fct_revenue)]
Illustrative — graph is built from your SQL CTEs and table references.