The need to compare data tools and to keep hunting for the perfect one seems never-ending. In this blog, We’re going to see the comparison between the Apache Beam and Apache Airflow.
This blog helps you choose by looking into the differences and similarities between the two: Apache Airflow and Apache Beam.
On the surface, Apache Airflow and Apache Beam may look similar.
Both are open-source, too. And were designed to organize the steps of processing the data.
A DAG is a Directed Acyclic Graph —
A conceptual representation of a series of activities. And a mathematical abstraction of a data pipeline.
Visualize the stages and dependencies in the form of directed acyclic graphs (DAGs) through a (GUI).
Principles Followed by Apache Airflow
Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Dynamic: This allows for writing code that instantiates pipelines dynamically.
Extensible: Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment.
Elegant: Airflow pipelines are lean and explicit.
Apache Airflow Overview
In 2015, Airbnb experienced a problem. They were growing like crazy and had a massive amount of data that was only getting larger.
To achieve the vision of becoming a fully data-driven organization, they had to grow their workforce of data engineers, data scientists, and analysts — all of whom had to regularly automate processes by writing scheduled batch jobs.
To satisfy the need for a robust scheduling tool, Maxime Beauchemin created an open-sourced Airflow with the idea that it would allow them to quickly author, iterate on, and monitor their batch data pipelines.
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows.
Airflow was originally created to solve the issues that come with long-running cron tasks and hefty scripts.
- Code-first: Workflows defined as code are easier to test, maintain, and collaborate on.
- Rich UI: The user interface is really intuitive and a truly practical way to access task metadata.
- Availability: you can set up and run Airflow on-premises.
- Being data processing agnostic: Airflow does not make any assumptions on how the data is processed by any of the myriads of services it uses.
- Integrations: ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc).
DAG represents a collection of tasks you want to run. And organized to show relationships between tasks in Airflow’s UI.
Tasks represent each node of a defined DAG.
Operators are the building blocks of Airflow and determine the actual work.
Hooks are Airflow’s way of interfacing with third-party systems. They allow you to connect to external APIs and databases like Hive, S3, GCS, MySQL, Postgres, etc.
Providers are community-maintained packages that include all of the core
Hooks for a given service (e.g. Amazon, Google, Salesforce, etc.)
Plugins represent a combination of Hooks and Operators.
Connections are where Airflow stores information that allows you to connect to external systems, such as authentication credentials or API tokens.
Apache Beam Overview
Apache Beam is more of an abstraction layer than a framework.
It serves as a wrapper for Apache Spark, Apache Flink, Google Cloud Dataflow, and others, supporting a more or less similar programming model.
The intent is that once someone learns Beam, they can run on multiple backends without getting to know them well.
Beam creates batch and streaming data processing jobs, becoming an engine for dataflow, and also basing the process on DAGs.
The DAG nodes create a (potentially branching) pipeline.
The DAG nodes are all active simultaneously, passing data pieces from one to the next as each performs some processing on it.
One of the main reasons to use Beam is the ability to switch between multiple runners such as Apache Spark, Apache Flink, Samza, and Google Cloud Dataflow.
Without Beam, a unified programming model, varied runners have different capabilities, making it difficult to provide a portable API.
Beam attempts to strike a delicate balance by actively incorporating advances from these runners into the Beam model while simultaneously engaging with the community to influence these runners’ roadmaps.
The Direct Runner runs pipelines to ensure that they comply with the Apache Beam paradigm as precisely as possible. Runners are provided with smarts – the more, the better.
Airflow and Apache Beam can both be classified as “Workflow Manager” tools. However, the better you get to know them, the more different they become.
Apache Beam is a fantastic option for autoscaled real-time pipelines and huge batches in machine learning.
Airflow, on the other hand, is perfect for data orchestration.