본문으로 바로가기

홈 Data Engineering

강의

Building Data Pipelines with Airflow

고급기술 수준

업데이트됨 2026. 6.

Author Dags with the TaskFlow API, asset-based scheduling, and deferrable sensors, and run an end-to-end SQL ETL pipeline with quality checks.

무료로 강의 시작

AirflowData Engineering

4시간

16 동영상

60 연습 문제

4,500 XP

성취 증명서

수천 개 기업의 학습자들이 사랑하는

팀을 교육하시나요?

비즈니스용으로 체험해 보세요

강의 설명

Want to take your Airflow skills further? This course uses Airflow 3.2, so you'll learn the latest way of doing things. You'll author Dags with the TaskFlow API, schedule them based on data using Assets and the new Asset Partitions, and make them reliable with retries, callbacks, and tests.In the final chapter, you'll build an end-to-end SQL ETL pipeline on DuckDB and add data quality checks directly in Airflow, without any third-party libraries, so the data your pipeline produces stays trustworthy. By the end, you'll know how to take a pipeline from a prototype to something you can actually run in production.

선수 조건

Introduction to Apache Airflow in Python

1

Authoring Dags with TaskFlow and XCom

You'll start by meeting the Airflow components, writing your first Dags with the TaskFlow API, and passing data between tasks with XCom.

Airflow core concepts

Writing a minimal TaskFlow Dag

Matching Airflow components to their roles

The TaskFlow API

Converting a classic Dag to TaskFlow

Adding a task and wiring dependencies

Exploring a pipeline in the Airflow UI

Sharing data between tasks

Returning data from a TaskFlow function

Connecting a classic operator to a TaskFlow task

Understanding XCom limitations

2

Dynamic and Data-Aware Pipelines

From there, you'll run tasks in parallel with dynamic task mapping, schedule Dags by data with Assets, and add human approval steps.

Dynamic Task Mapping

Processing multiple endpoints with expand

Fixing a shared parameter with partial

Pairing inputs with zip

Data-aware scheduling with Assets

Signaling data availability with an Asset outlet

Scheduling a consumer Dag on an Asset

Seeing asset-based scheduling in action

Templating, idempotency, and backfilling

Adding date-aware templating

Making a pipeline idempotent

Triggering a backfill

Human-in-the-loop workflows

Collecting form input with HITLEntryOperator

Responding to a Required Action in the UI

Branching with HITLBranchOperator

3

Preparing Dags for Production

In this chapter, you'll handle failures with retries and callbacks, save resources with deferrable sensors, and test your Dags at three levels.

Robust Dags with failure handling and retries

Configuring retries with exponential backoff

Auto-pausing a failing Dag

Choosing the right failure handling strategy

Deferrable operators

Configuring a sensor for production

Making a sensor deferrable

When to use poke, reschedule, or deferrable

Testing Airflow code

Writing a Dag integrity test

Unit testing a task function

Integration testing with dag.test()

Organizing complex Dags with Task Groups

Grouping related tasks

Nesting task groups with display names

Organizing tasks by domain

4

Building a Production SQL ETL Pipeline

In this final chapter, you'll build a SQL ETL pipeline on DuckDB, add partition-aware scheduling with Asset Partitions, and embed data quality checks.

Running SQL workloads

Aggregating sales data with SQL

Parameterizing SQL and extracting it into files

Choosing the safe approach for user input

Partition-aware scheduling with Asset Partitions

Creating a partitioned pipeline

Scheduling on partitioned assets

Watching partitioned assets trigger a downstream Dag

Data quality checks

Adding column-level quality checks

Adding table-level quality checks

Catching data issues with the right operator

From development to production

Triggering a pipeline from the CLI

Backfilling historical data

Production challenges with Airflow

Congratulations!

Building Data Pipelines with Airflow

강의
완료

수료증 획득

LinkedIn 프로필, 이력서 또는 CV에 이 인증서를 추가하세요
소셜 미디어와 성과 평가에서 공유하세요지금 등록

19백만 명 이상의 학습자와 함께 Building Data Pipelines with Airflow을(를) 시작하세요!

DataCamp for Mobile을 통해 데이터 분석 능력을 향상시키세요.

모바일 강좌와 매일 5분 코딩 챌린지를 통해 이동 중에도 학습 효과를 높이세요.