Workflow Management with Apache Airflow


Course Number: PYTH-248WA
Duration: 2 days (13 hours)
Format: Live, hands-on

Airflow Management Training Overview

Apache Airflow is an open-source, Python-based solution that allows developers to programmatically author, schedule, and monitor complex workflows. This Workflow Management with Apache Airflow training course teaches attendees how to manage workflows successfully and incorporate sophisticated automation techniques into their processes.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

  • Work in the Airflow environment
  • Understand Airflow DAG
  • Schedule Jobs
  • Work with backfilling
  • Pass Parameters
  • Perform XCom messaging
  • Understand task branching
  • Understand re-tries
  • Use SimpleHttpOperator

Prerequisites

Participants must have some familiarity with Python or have a programming background.

Outline

Expand All | Collapse All

Apache Airflow Introduction
  • A Traditional ETL Approach
  • Apache Airflow Defined
  • Airflow Core Components
  • The Component Collaboration Diagram
  • Workflow Building Blocks and Concepts
  • Airflow CLI
  • Main Configuration File
  • Extending Airflow
  • Jinja Templates
  • Variables and Macros
Apache Airflow Web UI
  • Web UI - the Landing (DAGs) Page
  • Web UI - the DAG Graph View
  • Run Status Legends
  • The Pause Button (Trigger Latch)
  • The DAG Triggering/Job Checking Sequence
  • The Control Panel for a Task
  • Sample Log File Messages (Abridged for Space)
Anatomy of a DAG and Scheduling
  • What is a DAG?
  • Scheduled and Manually Triggered DAG Runs
  • The DAG Object
  • Tasks
  • Task Lifecycle
  • Operators
  • Idempotent Operators
  • Operator Types
  • Airflow Common Operators
  • Specifying Dependencies
  • Associating Operators with a DAG
  • Associating Operators Using the "With DAG" Statement Example
  • Associating Operators with DAG Using the Operator's Constructor
  • The default_args Parameter
  • Passing DAG Parameters Through Web UI
  • DAG Run Scheduling
  • Examples of the schedule_interval Parameter
  • DAG Scheduling Nuances
  • Understanding The Backfill Process
  • Killing/Stopping DAG Runs
  • An XCom Messaging Example
Conclusion

Training Materials

All Apache Airflow training students receive comprehensive courseware.

Software Requirements

  • Python 3.5 or later
  • Airflow 2.1 or later


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