ZenML vs. Apache Airflow: A Comparative Analysis for MLOps

ZenML takes a very different approach to MLOps than other solutions. We frequently get asked why engineering teams should select ZenML versus Apache Airflow.

ZenML simplifies managing end-to-end machine learning workflows, focusing on ease of use, reproducibility, and scalability. It's designed specifically for data scientists and ML engineers who want to streamline their processes without getting bogged down by complex configurations.

On the other hand, Apache Airflow is a powerful and widely-adopted tool known for its flexibility and extensibility. Originally built for orchestrating general workflows, Airflow has become popular in various domains, including data engineering and machine learning, due to its ability to define and manage complex pipelines. Its extensive plugin ecosystem and large community make it a versatile option for those who need to integrate a wide range of tools and services.

In this blog, we will compare ZenML and Apache Airflow, examining their strengths, use cases, and how they stack up against each other in the context of managing machine learning workflows.

What is orchestration?

Workflow orchestration involves the automated arrangement, coordination, and management of complex tasks across computer systems and services. Its primary goal is to ensure that processes are executed in the correct order and at the right time, minimizing manual intervention and errors. Orchestration is crucial across various fields. In data engineering and ETL pipelines, it ensures that data flows smoothly, transformations are applied correctly, and processes run efficiently from source to destination. In DevOps, orchestration automates the deployment, scaling, and management of applications, streamlining CI/CD pipelines and simplifying the management of complex microservices architectures. It also plays a key role in cloud computing, managing the deployment and scaling of cloud resources to optimize utilization and reduce costs. Additionally, in IT operations, orchestration automates routine tasks like system monitoring, backups, and patch management, keeping systems secure and up-to-date.Orchestration is vital for managing the intricate steps of developing, training, and deploying machine learning (ML) models. Through MLOps practices, orchestration ensures that ML workflows are streamlined, scalable, and integrated with existing IT and DevOps processes, reducing errors and accelerating model deployment. By automating these complex workflows, orchestration not only enhances efficiency but also ensures that ML models are maintained and updated consistently, making it a critical component in the production lifecycle of machine learning projects.

Orchestration In MLOps:

Selecting the right MLOps tool is crucial for organizations to streamline and scale their ML workflows, impacting efficiency, effectiveness, and business outcomes as the right tool helps in:

1. Pipeline Automation and Workflow Efficiency

Tools like Airflow, Kubeflow, and ZenML automate and orchestrate ML pipelines, streamlining the entire ML lifecycle from data ingestion and preprocessing to model training, deployment, and monitoring. These platforms enable efficient resource management and faster iteration cycles, as well as ensure continuous model performance in production environments.

2. Scalable Infrastructure and Resource Management

Collaboration and Reproducibility: MLOps platforms like MLflow and Kubeflow enhance team collaboration by providing shared environments and integrated version control. They ensure reproducibility by tracking datasets, code, model versions, and experiment configurations. For example, MLflow logs experiments and parameters, while Kubeflow offers detailed lineage tracking, ensuring models can be consistently reproduced and audited across different stages of the ML lifecycle.

3. Collaborative Development and Reproducible Workflows

MLOps tools, like MLflow and Kubeflow, enhance team collaboration through shared environments and integrated version control. They ensure reproducibility by tracking data, code, and model versions. For instance, MLflow logs all experiments, including parameters and results, while Kubeflow provides detailed lineage tracking. This allows teams to reproduce and audit models consistently across development and production stages, maintaining consistency and reliability.

4. Observability and Continuous Model Governance

MLOps platforms, such as SageMaker and Databricks, offer real-time insights into model performance, enabling teams to monitor critical metrics like accuracy and latency continuously. They also ensure compliance with regulatory standards by providing robust auditing and logging features. For example, SageMaker includes built-in tools for tracking model drift, while Databricks supports automated compliance checks, helping organizations meet stringent regulatory requirements.

5. Elasticity and Interoperability

MLOps tools like Kubeflow and Airflow seamlessly integrate with existing systems and support deployment across multiple environments, including cloud, on-premises, and edge. For example, Kubeflow integrates with Kubernetes for scalable deployments, while Airflow connects with various data sources and cloud services, enabling flexible, multi-environment workflows that adapt to diverse infrastructure needs.

6. Security and Model Integrity

MLOps platforms like Azure Machine Learning and AWS SageMaker ensure robust data and model security, safeguarding against adversarial attacks and intellectual property theft. For example, Azure Machine Learning offers built-in security features like encryption and role-based access control. At the same time, SageMaker provides model monitoring and anomaly detection to protect sensitive models and data throughout the ML lifecycle.

Community Highlight

Apache Airflow, initially developed by Airbnb and later adopted by the Apache Software Foundation, is widely used for orchestrating complex workflows, particularly in data pipelines and ETL processes. It has gained a strong community known for its active engagement across various platforms and offers a wealth of resources, making it a reliable choice for workflow orchestration. Airflow allows users to define workflows as directed acyclic graphs (DAGs) using Python, providing a flexible and scalable framework. Its robust ecosystem and community involvement in meetups and conferences further strengthen its position as a leading tool in the workflow orchestration space.

ZenML is an open-source MLOps framework that is rapidly growing in the machine learning operations space. Despite being newer and having a smaller community than established tools, ZenML's community is highly engaged and actively growing. The core team is very responsive, interacting with users through Slack, GitHub Discussions, and webinars. ZenML's comprehensive documentation and tutorials make it easy for users to get started and contribute. The contribution-friendly community fosters collaboration and innovation and is designed to integrate seamlessly with popular ML libraries, tools, and infrastructure. ZenML emphasizes simplicity, modularity, and extensibility, enabling data scientists and engineers to build reproducible and production-ready ML pipelines. As ZenML continues to gain traction, it is becoming an excellent choice for those focusing on modern MLOps.

Both tools automate workflows but cater to different needs. ZenML is designed for machine learning operations, where workloads often involve compute-intensive tasks like model training. It abstracts away infrastructure complexities, allowing data scientists and ML engineers to focus on resources like GPUs, memory, and CPUs without worrying about underlying systems. In contrast, Apache Airflow is built to orchestrate less compute-intensive workflows, such as data pipelines, and is favored by data engineers who are more concerned with concepts like backfills, schedules, and transformations. This makes ZenML ideal for ML tasks and Airflow a solid choice for data pipeline orchestration.

Understanding Apache Airflow

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Its extensible Python framework enables you to build workflows connecting virtually any technology. A web interface helps manage the state of your workflows. Airflow is deployable in many ways, from a single process on your laptop to a distributed setup to support even the biggest workflows.

Architecture Diagram

Apache Airflow’s architecture consists of several core components:

1. Scheduler: Responsible for scheduling jobs and ensuring tasks are executed in the correct order based on dependencies.
2. Executor: Manages the execution of tasks, which can be handled locally or by distributed systems.
3. Workers: Execute the tasks defined in the DAGs, which can run on different systems depending on the executor used.
4. Metadata Database: Stores information about DAGs, task instances, users, etc.
5. Web Server: Provides a user interface to monitor and manage workflows.
6. CLI: Allows for interaction with the Airflow environment through the command line.

Key Features

1. Dynamic: Airflow pipelines are configured as code (Python), allowing for dynamic pipeline generation. This will enable users to write code that instantiates pipelines dynamically.
2. Extensible: Easily define your operators and executors and extend the library to fit the level of abstraction that suits your environment.
3. Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the Jinja templating engine.
4. Scalable: Airflow has a modular architecture and uses a message queue to communicate with and orchestrate an arbitrary number of workers.

Use Cases

• Scheduling and Orchestrating Data Pipelines: Airflow enables users to schedule, manage, and monitor complex data workflows as Directed Acyclic Graphs (DAGs), automating data flow between systems and ensuring correct task execution order.
• ETL/ELT Pipeline Management: Airflow is commonly used for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. It helps extract data, apply business rules for transformation, and load into data warehouses or other destinations.
• Parallel Task Execution: Airflow allows for parallel task execution, optimizing resource use and reducing workflow completion time. This is particularly useful in scenarios where multiple independent tasks can be run simultaneously, such as the pizza-making analogy with kneading dough and preparing sauce.
• Integration with Big Data Ecosystems: Airflow integrates natively with big data tools like Apache Hive, Presto, and Spark, making it an ideal choice for orchestrating jobs running on these engines.
• Improving Data Quality for Machine Learning Models: Airflow's well-structured data pipelines provide data scientists with complete and accurate datasets, leading to better machine learning models.

Where does Airflow fall short?

Using Apache Airflow as an MLOps orchestrator presents several challenges, as it was not originally designed with machine learning workflows in mind. Commonly cited issues include the lack of native support for ML-specific tasks, the complexity of integrating machine learning libraries, and the manual effort required for experiment tracking, model versioning, and deployment. While Airflow's flexibility and extensibility make it a powerful tool for general workflow orchestration, these same features can make it cumbersome and inefficient for MLOps use cases, as discussed frequently in community forums like Reddit. In the following sections, we'll explore these challenges in detail and how they impact the effectiveness of using Airflow for machine learning operations.

1. This Reddit post discusses why Apache Airflow, despite being widely used in data engineering, often receives mixed reviews. Many users appreciate its capabilities but acknowledge that it was not designed with machine learning (ML) pipelines in mind. This can lead to frustration when using Airflow for ML workflows, which are typically more compute-intensive and have different requirements than the data pipelines Airflow excels at managing. As a result, there's growing interest in exploring tools better suited for ML tasks.

2. In this one, people share their experiences and opinions of using Airflow for production-grade (highly scalable) ML pipelines. They agree that Airflow is a great tool, but it’s excruciating to debug/run locally. Even though it was made with data scientists in mind, there had to be an IT guy to use it with new tech like Kubernetes. It can be complex, with many things to learn before use.

Let's run Airflow locally to get some hands-on experience:

Install Prerequisites

• Ensure you have Python (3.6, 3.7, or 3.8) installed.
• Ensure Docker is installed with docker-compose.
• Optionally, set up a virtual environment to isolate your Airflow installation

python3 -m venv airflow_venv
source airflow_venv/bin/activate

‍Install Apache Airflow

mkdir airflow-docker
cd airflow-docker
curl -LfO https://airflow.apache.org/docs/apache-airflow/stable/docker-compose.yaml
mkdir ./dags ./plugins ./logs

In MacOS or Linux, you need to take folder permissions

echo -e “AIRFLOW_UID=$(id -u)\\nAIRFLOW_GID=0” > .env

Run Airflow

docker-compose up airflow-init
docker-compose up

Run a custom pipeline

cd dags
curl -LJO https://raw.githubusercontent.com/Sid-Lais/Airflow-Test/main/airflow_pipeline.py

‍

Open your web browser and go to localhost:8080. Both default username and password are airflow. The custom pipeline will be shown there.

Understanding ZenML

ZenML is an open-source MLOps framework designed to simplify the development, deployment, and management of machine learning workflows. It emphasizes best reproducibility, scalability, and automation practices, making it easier for data scientists and ML engineers to build production-ready models.

Key Features

1. Glue: ZenML integrates seamlessly with your preferred tools, including orchestrators like Apache Airflow, combining the flexibility of custom stacks with the power of existing infrastructure.
2. Modularity: ZenML adopts a modular approach for creating ML workflows, breaking them into smaller, reusable steps to promote code reusability, maintainability, and scalability.
3. Standardization: ZenML simplifies ML practices for teams and organizations, offering a uniform framework for developing and deploying ML workflows to enhance collaboration and streamline processes.
4. Reproducibility: ZenML makes it easy to reproduce ML workflows, ensuring consistent results. It tracks data and model versions, making it effortless to recreate experiments and verify results.
5. Machine Learning Specificity: ZenML is explicitly tailored for ML pipelines, with built-in features for tracking models, artifacts, and metadata unique to machine learning. These features provide a more focused and efficient workflow for ML tasks.
6. Ease of Transition: ZenML is easy to use locally in a Jupyter notebook, allowing for seamless experimentation. When it's time to move to production, ZenML supports a smooth transition, simplifying scaling and deploying workflows in a production environment.

Use Cases

1. End-to-End Machine Learning Pipelines:
   • ZenML excels at orchestrating complete ML workflows, ensuring each step is reproducible and scalable. This makes it ideal for managing complex, multi-stage ML projects.
2. Experiment Tracking and Reproducibility:
   • The framework is particularly effective for scenarios where maintaining experiment reproducibility and consistency across different environments is critical.
3. Seamless Integration with MLOps Ecosystem:
   • ZenML is well-suited for environments requiring integration with various MLOps tools, allowing teams to leverage a mix of best-of-breed solutions while maintaining a cohesive and manageable workflow.
4. Rapid Prototyping and Deployment:
   • ZenML supports quick prototyping and deployment of ML models, making it a valuable tool for teams looking to accelerate time-to-market while ensuring their models are robust and scalable.

ZenML's ability to run pipelines on orchestrators like Airflow offers the best of both worlds, combining flexibility with the power of established tools, making it an essential tool for modern ML teams.

Now, let's run the same pipeline in ZenML

Learn how to set up and use ZenML to run machine learning pipelines locally for faster development and model refinement. Let's start by setting up your local ML pipeline using ZenML.

Set-up ZenML

Run your first pipeline:

• Clone the quickstart example to your local machine and experience smooth integration with your existing technology stack

curl -LJO https://raw.githubusercontent.com/Sid-Lais/ZenML-Test/main/zenml_pipeline.py

• Initialize ZenML in the current directory

zenml init

• Run the model training pipeline

python zenml_pipeline.py

Once it is running, your dashboard will show all the details of the associated run, models,

and artifacts.

ZenML vs. Apache Airflow: Side-by-Side Code Comparison

Below is a side-by-side comparison of how ZenML and Apache Airflow handle a basic machine learning pipeline. The example will illustrate defining, scheduling, and running a simple pipeline in both frameworks.

Basic Pipeline Setup

ZenML Code

Here is an example of how you could write a simple machine learning pipeline with ZenML. Since the code remains the same no matter what infrastructure or orchestration backend you use, this code could run on Airflow as is.

from zenml import pipeline, step
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Step to load data
@step
def load_data() -> tuple:
    iris = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

# Step to train the model
@step
def train_model(X_train: list, y_train: list) -> RandomForestClassifier:
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    return model

# Step to evaluate the model
@step
def evaluate_model(model: RandomForestClassifier, X_test: list, y_test: list) -> float:
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    return accuracy

# Define the ZenML pipeline
@pipeline
def ml_workflow_pipeline():
    X_train, X_test, y_train, y_test = load_data()
    model = train_model(X_train, y_train)
    accuracy = evaluate_model(model, X_test, y_test)

# Run the ZenML pipeline
if __name__ == "__main__":
    ml_workflow_pipeline()

Airflow Code

If you were just using raw Airflow code, this is an example of what your pipeline might look like:‍

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def load_data(ti):
    iris = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
    ti.xcom_push(key='train_test_data', value=(X_train, X_test, y_train, y_test))

def train_model(ti):
    X_train, X_test, y_train, y_test = ti.xcom_pull(key='train_test_data', task_ids='load_data')
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    ti.xcom_push(key='model', value=model)

def evaluate_model(ti):
    model = ti.xcom_pull(key='model', task_ids='train_model')
    X_train, X_test, y_train, y_test = ti.xcom_pull(key='train_test_data', task_ids='load_data')
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    print(f"Model accuracy: {accuracy}")

with DAG("ml_workflow_dag",
         start_date=datetime(2023, 1, 1),
         schedule_interval=None,
         catchup=False) as dag:

    load_data_task = PythonOperator(
        task_id="load_data",
        python_callable=load_data
    )

    train_model_task = PythonOperator(
        task_id="train_model",
        python_callable=train_model
    )

    evaluate_model_task = PythonOperator(
        task_id="evaluate_model",
        python_callable=evaluate_model
    )

    load_data_task >> train_model_task >> evaluate_model_task

Code-Level Comparison:

1. Setup and Structure:
   • Airflow: Defines the workflow as a Directed Acyclic Graph (DAG), where each task is represented as a node. The tasks are explicitly defined and dependencies are managed using task IDs and XComs for data sharing between tasks.
   • ZenML: Utilizes a more modular approach with decorators (@step and @pipeline) to define steps and construct the pipeline. The pipeline is defined as a series of steps where outputs from one step can be directly passed to the next without additional configuration.
2. Task Definition:
   • Airflow: Tasks are created using operators like PythonOperator, and data is passed between tasks using XComs (cross-communication objects). This requires defining each task separately and managing data flow manually.
   • ZenML: Steps in the pipeline are defined as Python functions with the @step decorator, allowing for direct and automatic data passing between steps. This reduces the need for boilerplate code and makes the workflow easier to manage.
3. Data Handling:
   • Airflow: Data handling between tasks relies on XComs, where data needs to be pushed and pulled explicitly using task IDs. This adds complexity, especially in workflows that require frequent data exchange between tasks.
   • ZenML: Data is passed between steps naturally through function arguments and return values, making it more intuitive and less error-prone for managing data dependencies in ML workflows.
4. Execution:
   • Airflow: The DAG is scheduled to run automatically based on the defined schedule interval (e.g., daily, hourly). The Airflow scheduler manages task execution according to the dependencies and schedules defined in the DAG.
   • ZenML: Pipelines are executed by simply calling the pipeline function, without the need for an external scheduler unless integrated with other tools. This makes it more straightforward for one-off or development runs, though it can be scheduled externally if needed.
5. ML Workflow Specifics:
   • Airflow: While it can be used for ML workflows, it is not specifically designed for this purpose. Users often need to write custom code or integrate third-party tools to handle ML-specific tasks like model training, versioning, and deployment.
   • ZenML: Designed specifically for ML workflows, with built-in support for common ML tasks and a focus on reproducibility, model management, and seamless integration with ML frameworks and tools.

This comparison showcases how ZenML offers a more streamlined and intuitive approach for ML-specific workflows. Airflow provides greater flexibility and is better suited for general-purpose workflow orchestration across different domains.

ZenML vs. Apache Airflow

The main reasons why ML engineers should try ZenML over Apache Airflow are:

Cloud-Native Integration Comparison

Choosing the Right Tool

Airflow + ZenML = Airflow++

• Standalone Airflow is best for projects heavily focused on data engineering, ETL processes, and workflow automation across various systems and platforms.
• Standalone ZenML is ideal for MLOps-focused projects where reproducibility, model management, and integration with ML tools are the top priorities.
• Airflow + ZenML is a powerful combination for projects requiring data engineering and machine learning capabilities. This setup allows teams to manage complex data workflows with Airflow while using ZenML to handle the unique demands of MLOps, providing a comprehensive solution for end-to-end data and ML workflows.
• Steps to Run Pipelines with Airflow Orchestrator
• ZenML pipelines can run as Airflow DAGs, combining Airflow's orchestration with ZenML's ML-specific benefits. Each ZenML step runs in its own Docker container scheduled and started using Airflow.
• STEP 1: Integration

zenml integration install airflow

• STEP 2: Prerequisites

You will need Docker installed and running.

• STEP 3: The orchestrator registered and part of our active stack:

zenml orchestrator register ORCHESTRATOR_NAME --flavor=airflow --local=True

If you are using a remote Airflow deployment you can set --local=True to False.

Now register and activate a stack with the new orchestrator:

zenml stack register STACK_NAME -o ORCHESTRATOR_NAME ... --set

• Step 4: Set Up a Local Airflow Server Environment

Since running Airflow locally can sometimes lead to dependency conflicts, it’s recommended to create a separate virtual environment for the Airflow server:

python -m venv airflow_server_env
source airflow_server_env/bin/activate

Then install any needed requirements:

pip install "apache-airflow==2.4.0" "apache-airflow-providers-docker<3.8.0" "pydantic~=2.7.1"

• Step 5: Start the Local Airflow Server

Before running your ZenML pipeline, you need to start the local Airflow server. You can set environment variables to configure the Airflow server. For example:

export AIRFLOW_HOME=~/airflow
export AIRFLOW__CORE__DAGS_FOLDER=~/airflow/dags

Start the Airflow server:

airflow standalone

This will start the Airflow web server and scheduler locally. You can access the Airflow UI at http://localhost:8080.

• Step 6: Run a ZenML Pipeline

Now that Airflow is set up, you can run your ZenML pipeline. First switch back to the environment where ZenML is installed:

source your_zenml_env/bin/activate

Run the pipeline:

python my_pipeline_script.py

After running your pipeline, ZenML will create a .zip file representing it. This file will be stored in the logs directory. To enable Airflow to execute it, you must move the .zip file to the Airflow DAGs directory.

• Step 7: Copy the DAG to the Airflow DAGs Directory

By default, ZenML won't automatically place the DAG in the Airflow directory. You can either manually copy it or configure ZenML to do it for you. Switch to the Python environment that has ZenML installed before running these two commands:

zenml orchestrator update --dag_output_dir=
python file_that_runs_a_zenml_pipeline.py

‍

• You can now run any ZenML pipeline using the Airflow orchestrator:

Conclusion

Summary of Key Differences and Strengths

• Apache Airflow:
  • Strengths:
    • Best suited for orchestrating complex data engineering workflows, ETL processes, and task automation.
    • Offers a mature, well-established platform with extensive integration capabilities across various data tools and cloud services.
    • Strong community support and a rich ecosystem of plugins make it highly customizable and scalable for large-scale projects.
  • Key Difference: Primarily designed for general workflow orchestration, not explicitly tailored for MLOps.
• ZenML:
  • Strengths:
    • Focused on MLOps, offering a pipeline-centric approach that emphasizes reproducibility, modularity, and seamless integration with machine learning tools.
    • User-friendly setup and API make it accessible to ML teams, enabling quick development, deployment, and management of ML workflows.
    • Highly modular, allowing users to quickly adapt to changing ML project requirements and integrate with various MLOps tools.
  • Key Difference: It is specifically designed for managing the machine learning lifecycle, with features that support the unique needs of MLOps.

Final Thoughts on Making an Informed Decision

When deciding whether to use Apache Airflow, ZenML, or both, consider your project's specific needs and your team's focus. If you require data engineering, workflow orchestration, and task automation, Apache Airflow's robust architecture and extensive integration capabilities make it the better choice. However, if you're focused on managing machine learning workflows emphasizing reproducibility and smooth integration with ML tools, ZenML would be the more suitable option.

For projects involving data engineering and MLOps, combining Airflow and ZenML can provide a robust and comprehensive solution—Leverage Airflow’s scheduling and orchestration capabilities alongside ZenML’s ML-specific features to manage end-to-end workflows efficiently.

Ultimately, the right tool or combination of tools will depend on your team’s expertise, the complexity of your workflows, and the specific goals of your project. Carefully considering these factors will ensure that an informed decision will set your team up for success and ensure that your workflows are efficient and scalable.

We recommend trying each tool separately and comparing them based on your project needs. If you require features from both, you can always use them in conjunction to get the best of both worlds. Our team at ZenML is always open to suggestions to help simplify MLOps for you.

❓FAQ

1. Is Apache Airflow still relevant?

Airflow remains a popular framework for many companies trying to scale out their data pipeline infrastructure. Because it is easy to start and has a strong community, I foresee it will continue to play a major role in the data engineering space.

    2. Is Apache Airflow an orchestration tool?

Apache Airflow is an orchestration tool widely used to manage, schedule, and monitor complex workflows. Airflow allows users to define tasks and their dependencies as Directed Acyclic Graphs (DAGs), automating various data processing, machine learning, and ETL (Extract, Transform, Load) tasks across different environments and systems.

    3. Is Airflow an ETL?

Apache Airflow is not an ETL (Extract, Transform, Load) tool but is commonly used to orchestrate and manage ETL processes. Airflow allows you to define, schedule, and monitor the steps involved in ETL workflows, such as extracting data from various sources, transforming it according to business rules, and loading it into target systems like data warehouses. While Airflow provides the framework for running ETL tasks, the actual data extraction, transformation, and loading are performed by custom scripts or third-party tools integrated into the Airflow workflow.

    4. Is Airflow a DevOps tool?

Apache Airflow is not specifically a DevOps tool but can be used within DevOps workflows for automation and orchestration. Airflow is primarily designed to manage and schedule complex workflows and data pipelines, which can be a part of broader DevOps processes. For example, in a CI/CD pipeline, Airflow can be used to automate tasks like data processing, testing, and deployment steps. While it isn't a traditional DevOps tool like Jenkins or Kubernetes, its flexibility and scheduling capabilities make it a valuable asset in automating tasks crucial to DevOps practices.