Understanding Apache Airflow Streams Data Simplified 101

Davor DSouza • Last Modified: February 17th, 2023

Apache Airflow is a powerful and widely used open-source Workflow Management System (WMS) that can author, schedule, orchestrate, and monitor data pipelines and workflows programmatically. Airflow allows you to manage your data pipelines by authoring workflows as task-based Directed Acyclic Graphs (DAGs).

Apache Airflow streams data? NO, it is not a streaming solution. Tasks do not transfer data from one to the other (though they can exchange metadata!). Airflow is not in the same league as Spark Streaming or Storm but is more akin to Oozie or Azkaban.

There is no notion of data input or output – only flow. You manage task scheduling as code and can visualize the dependencies, progress, logs, code, trigger tasks, and success status of your data pipelines.

Apache Airflow, which is written in Python, is becoming increasingly popular, particularly among developers, due to its emphasis on configuration as code. Proponents of Airflow believe it is distributed, scalable, flexible, and well-suited to handling the orchestration of complex business logic.

In this article, you’ll come to know how Apache Airflow streams data and learn ideal use cases for Airflow and its features respectively.

Table of Contents


  • Data Pipeline Basics

What is Apache Airflow?

Airflow Streams - Apache Airflow logo
Image Source

Apache Airflow is a batch-oriented, pipeline-building open-source framework for developing and monitoring data workflows. Airbnb created Airflow in 2014 to solve big data and complex Data Pipeline problems. They wrote and scheduled processes, as well as monitored workflow execution, using a built-in web interface. The Apache Software Foundation adopted the Airflow project due to its growing success.

Apache Airflow allows users to efficiently build scheduled Data Pipelines by leveraging some standard Python framework features, such as data time format for task scheduling. It also provides a plethora of building blocks that allow users to connect the various technologies found in today’s technological landscapes.

Another important feature of Airflow is its backfilling capability, which allows users to easily reprocess previous data. Users can also use this feature to recompute any dataset after modifying the code. In practice, Airflow can be compared to a spider in a web: it sits at the heart of your data processes, coordinating work across multiple distributed systems.

Key Features of Apache Airflow

  • Dynamic: Airflow pipelines are coded (Python), allowing for dynamic pipeline generation. This enables the creation of code that dynamically instantiates pipelines.
  • Extensible: You can easily define your operators and executors, and you can extend the library to fit the level of abstraction that best suits your environment.
  • Elegant: Airflow pipelines are short and to the point. The powerful Jinja templating engine is used to parameterize your scripts, which is built into the core of Airflow.
  • Scalable: Airflow has a modular architecture and orchestrates an arbitrary number of workers using a message queue. The airflow is ready to expand indefinitely.

Simplify Data Analysis with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources (including 30+ free data sources) like Asana and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.


Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

How Apache Airflow Streams Data?

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows via DAGs ( Directed Acyclic Graphs ). In other words, an Airflow is a tool that allows you to automate simple and complex processes through the use of a rich UI interface that can execute them regularly.

Apache Airflow Workflows with directional dependencies are known as Directed Acyclic Graphs. In the figure below, for example, we can see that Task 3 is dependent on Task 2, which is dependent on two tasks, Task 1. a and Task 1. b. This is referred to as a workflow, and Airflow represents workflows using DAGs.

Airflow Streams - Task Scheduling
Image Source

Apache Airflow streams data in a horizontally scalable, distributed workflow management system that enables teams and developers to define complex workflows in Python code. The Airflow scheduler executes your tasks on an array of workers while adhering to the dependencies you specify.

Airflow Streams - DAG
Image Source

When is it Appropriate to use Apache Airflow Streaming?

Airflow Streams - ETL
Image Source

Data Pipelines are becoming more complex as data is pulled in from various sources at different stages of the pipeline. Workflow management systems, such as Apache Airflow, make the job of the data engineer easier by automating tasks.

Apache Airflow enables easy monitoring and failure handling to keep track of the dependency of a live intelligent system assisting in the data-productionization. pipeline’s

Furthermore, Apache Airflow allows for simple integration with Jupyter notebooks, as well as easy parameterization using tools such as paper mill, which aids in hyperparameter testing for a model. Airflow can use Canary testing to ensure that new models are superior to older models, which is useful in the productionization of ML models.

Creating Apache Airflow Streaming Data Pipelines

To keep things simple, we’ll use a sequential data pipeline. Within our DAG, we have the following components for Apache Airflow:

Airflow Streaming Step 1: Use a BashOperator to run an external Python script that consumes the Kafka stream and dumps it to a specified location.

Airflow Streaming Step 2: Using the cURL command and a BashOperator, download the IMDB dataset to a specified location.

Airflow Streaming Step 3: Run a data cleaning procedure. Python function that reads these files and converts them to the required format using a PythonOperator.

Airflow Streaming Step 4: Using a PythonOperator, upload the processed training data to an S3 bucket.

Airflow Streaming Step 5: Clean up the raw and processed data with a BashOperator and a bash command

Airflow Streams - Code for Setting up Data Streaming
Image Source
Airflow Streams - Graphical Representation of Airflow Streams
Image Source

Pitfalls of Airflow Streaming

  • The Execution date is a strict time requirement for Apache Airflow. No DAG can run in the absence of an execution date, and no DAG can run twice for the same execution date.
  • The Scheduler handles scheduled jobs and backfill jobs separately. This can lead to strange results, such as backfills failing to respect a DAG’s max active runs configuration.
  • If you want to change the schedule, you must rename your DAG. Because the previous task instances will no longer correspond to the new interval.
  • It is not simple to run Apache Airflow natively on Windows. However, by utilizing Docker, this can be mitigated.


In this article, you learned how Apache Airflow streams data and the best use cases for Airflow and its features. Apache Airflow’s strong Python framework foundation enables users to easily schedule and run any complex Data Pipelines at regular intervals. Data Pipelines, represented as DAG, is critical in Airflow for creating flexible workflows.

The rich web interface of Airflow allows you to easily monitor the results of pipeline runs and debug any failures that occur. Many companies have benefited from Apache Airflow today because of its dynamic nature and flexibility.

Visit our Website to Explore Hevo

Companies need to analyze their business data stored in multiple data sources. The data needs to be loaded to the Data Warehouse to get a holistic view of the data. Hevo Data is a No-code Data Pipeline solution that helps to transfer data from 100+ sources to desired Data Warehouse. It fully automates the process of transforming and transferring data to a destination without writing a single line of code.

Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of learning How Apache Airflow Streams Data in the comments section below!

No-code Data Pipeline for Your Data Warehouse