Introduction to Airflow for Data Engineering

As data engineering becomes more complex, with more data sources and greater volume of data, it's important to have an efficient way to manage and coordinate data workflows. Airflow is an open-source platform that allows data engineers to automate, schedule and monitor data workflows.

What is Airflow?

Airflow is an open-source platform to programmatically author, schedule and monitor workflows. It was originally developed at Airbnb and is now managed and maintained by the Apache Software Foundation. Airflow is highly customizable, and provides a powerful and flexible way to author, schedule, and monitor data workflows.

Airflow's main features include:

• A web interface for managing, monitoring and controlling workflows
• A command line interface for interacting with Airflow
• A metadata database for tracking the status and history of workflows
• A scheduler for executing workflows on a regular schedule
• A rich set of operators for executing a variety of data transformation and processing tasks
• A powerful templating system for creating dynamic workflows

In this blog post, we'll explore some of the key concepts and features of Airflow as they relate to data engineering workflows.

Key Concepts in Airflow

DAGs

At the heart of Airflow is the "Directed Acyclic Graph" or DAG. A DAG is a collection of tasks that are organized into a directed, acyclic graph. Tasks in a DAG have dependencies on other tasks, and Airflow uses these dependencies to determine task execution order.

Operators

Operators are the building blocks of tasks in Airflow. An operator represents a single, atomic step in a workflow. Operators can perform a variety of tasks, such as moving data between systems, transforming data, or executing code.

Airflow includes a rich set of operators for working with various data sources and performing common data transformation tasks. These operators can be combined into complex workflows to automate data processing.

Tasks

A task in Airflow represents a unit of work that needs to be performed. Tasks are attached to operators and organized into DAGs. Tasks can have dependencies on other tasks, and Airflow uses these dependencies to determine the execution order of tasks.

Sensors

A sensor is a special type of operator that waits for a particular event or condition to occur before proceeding with a workflow. Sensors are often used to wait for data to become available or a particular condition to be true.

Connections

Airflow supports a wide variety of connections to data sources, providing a flexible way to access data. Connections can be used by operators to read data from or write data to external systems.

Sample Airflow Workflow

To give you an idea of how Airflow might be used in practice, let's consider an example workflow for processing sales data from an e-commerce site.

The workflow involves several steps, including:

1. Extracting sales data from an external system
2. Transforming the data to add additional fields and clean the data
3. Loading the data into a database for further analysis

Here's how this workflow might be represented as a DAG in Airflow:

from airflow import DAG
from airflow.operators import PythonOperator, BashOperator
from airflow.contrib.operators import SalesforceToGoogleCloudStorageOperator
from datetime import datetime, timedelta
 
default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2022, 6, 15),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 5,
    'retry_delay': timedelta(minutes=5)
}
 
dag = DAG('ecommerce_sales', 
          default_args=default_args,
          catchup=False,
          schedule_interval=timedelta(days=1))
 
# Step 1: Extract data from Salesforce
extract_sales = SalesforceToGoogleCloudStorageOperator(
    task_id='extract_sales',
    salesforce_conn_id='salesforce',
    bucket='ecommerce-data',
    object_name='sales-data/{{ds}}/sales.json',
    dag=dag
)
 
# Step 2: Transform the data
transform_sales = BashOperator(
    task_id='transform_sales',
    bash_command='python /path/to/transform.py \
                  --input-file {{ti.xcom_pull("extract_sales")}} \
                  --output-file sales-transformed-{{ds}}.json',
    dag=dag,
)
 
# Step 3: Load the data into BigQuery
load_sales = BashOperator(
    task_id='load_sales',
    bash_command='bq load --source_format NEWLINE_DELIMITED_JSON \
                  myproject:ecommerce.sales \
                  gs://ecommerce-data/sales-data/{{ds}}/sales-transformed-{{ds}}.json',
    dag=dag,
)
 
# Define task dependencies
extract_sales >> transform_sales >> load_sales

In this example, the DAG has three tasks:

1. extract_sales: Uses a custom operator to extract sales data from Salesforce and store it in Google Cloud Storage.
2. transform_sales: Uses a BashOperator to run a Python script that performs data transformation on the extracted data.
3. load_sales: Uses another BashOperator to load the transformed data into BigQuery for further analysis.

The >> operator is used to define the dependencies between tasks, indicating that extract_sales must complete before transform_sales can begin, and transform_sales must complete before load_sales can begin.

Conclusion

Airflow is a powerful tool for managing, scheduling, and monitoring data workflows. By breaking down complex workflows into smaller tasks and using operators to automate data transformation and processing, data engineers can build robust, scalable workflows that are easy to maintain and manage.

To get started with Airflow, check out the Apache Airflow website for more information and tutorials. Category: Airflow