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Azure Functions VS Apache Airflow

Compare Azure Functions VS Apache Airflow and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Azure Functions logo Azure Functions

Azure Functions is a serverless event driven experience that extends the existing Azure App Service platform.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Azure Functions Landing page
    Landing page //
    2022-09-26
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Azure Functions features and specs

  • Scalability
    Azure Functions offers automatic scaling based on demand, which means it can handle varying workloads without manual adjustments.
  • Cost-effective
    With its consumption-based pricing model, you only pay for the compute resources used during function execution, making it cost-effective for many scenarios.
  • Simplified Development
    Developers can focus on writing code without worrying about infrastructure management, including server provisioning and maintenance.
  • Multiple Language Support
    Azure Functions supports a wide range of programming languages, including C#, JavaScript, Python, and more, offering flexibility for developers.
  • Integration with Azure Services
    Azure Functions seamlessly integrates with other Azure services like Cosmos DB, Blob Storage, and Event Hubs, enabling the creation of complex workflows.

Possible disadvantages of Azure Functions

  • Cold Start Latency
    Functions in a serverless environment can experience latency during initial invocation after being dormant, which might impact performance for time-sensitive applications.
  • Execution Timeout
    Azure Functions have a maximum execution timeout, which can be limiting for long-running processes and might require architectural adjustments.
  • Vendor Lock-In
    Leveraging Azure-specific features can lead to vendor lock-in, making it difficult to migrate to other cloud platforms without significant refactoring.
  • Limited Debugging and Monitoring
    Debugging Azure Functions can be less straightforward compared to traditional applications, with constraints in local debugging and monitoring capabilities.
  • Complex Pricing Model
    The pricing model can become complex, especially for applications with unpredictable workloads, making it challenging to estimate costs accurately.

Apache Airflow features and specs

  • Scalability
    Apache Airflow can scale horizontally, allowing it to handle large volumes of tasks and workflows by distributing the workload across multiple worker nodes.
  • Extensibility
    It supports custom plugins and operators, making it highly customizable to fit various use cases. Users can define their own tasks, sensors, and hooks.
  • Visualization
    Airflow provides an intuitive web interface for monitoring and managing workflows. The interface allows users to visualize DAGs, track task statuses, and debug failures.
  • Flexibility
    Workflows are defined using Python code, which offers a high degree of flexibility and programmatic control over the tasks and their dependencies.
  • Integrations
    Airflow has built-in integrations with a wide range of tools and services such as AWS, Google Cloud, and Apache Hadoop, making it easier to connect to external systems.

Possible disadvantages of Apache Airflow

  • Complexity
    Setting up and configuring Apache Airflow can be complex, particularly for new users. It requires careful management of infrastructure components like databases and web servers.
  • Resource Intensive
    Airflow can be resource-heavy in terms of both memory and CPU usage, especially when dealing with a large number of tasks and DAGs.
  • Learning Curve
    The learning curve can be steep for users who are not familiar with Python or the underlying concepts of workflow management.
  • Limited Real-Time Processing
    Airflow is better suited for batch processing and scheduled tasks rather than real-time event-based processing.
  • Dependency Management
    Managing task dependencies in complex DAGs can become cumbersome and may lead to configuration errors if not properly handled.

Azure Functions videos

+ Add

How to build a movie review app with Azure Cosmos DB and Azure Functions | Azure Makers Series

More videos:

  • Review - Go serverless: Event-driven applications with Azure Functions | Azure Friday
  • Review - Azure Friday | Serverless Apps with Azure Cosmos DB and Azure Functions

Apache Airflow videos

+ Add

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Azure Functions and Apache Airflow)

Azure Functions

Apache Airflow

Cloud Hosting
100 100%
Cloud Hosting
0% 0
Workflow Automation
0 0%
Workflow Automation
100% 100
Cloud Computing
100 100%
Cloud Computing
0% 0
Automation
0 0%
Automation
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Azure Functions and Apache Airflow

Azure Functions Reviews

Top 7 Firebase Alternatives for App Development in 2024
Azure Functions is particularly useful for developers working with .NET technologies or those already using Azure services.
Source: signoz.io

Apache Airflow Reviews

5 Airflow Alternatives for Data Orchestration
While Apache Airflow continues to be a popular tool for data orchestration, the alternatives presented here offer a range of features and benefits that may better suit certain projects or team preferences. Whether you prioritize simplicity, code-centric design, or the integration of machine learning workflows, there is likely an alternative that meets your needs. By...
Source: www.kdnuggets.com
Top 8 Apache Airflow Alternatives in 2024
Apache Airflow is a workflow streamlining solution aiming at accelerating routine procedures. This article provides a detailed description of Apache Airflow as one of the most popular automation solutions. It also presents and compares alternatives to Airflow, their characteristic features, and recommended application areas. Based on that, each business could decide which...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped you explore the best Apache Airflow Alternatives available in the market. So, you can try hands-on on these Airflow Alternatives and select the best according to...
Source: hevodata.com
A List of The 16 Best ETL Tools And Why To Choose Them
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Apache Airflow programmatically creates, schedules and monitors workflows. It can also modify the scheduler to run the jobs as and when required.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, Apache Airflow seems to be a lot more popular than Azure Functions. While we know about 75 links to Apache Airflow, we've tracked only 3 mentions of Azure Functions. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Azure Functions mentions (3)

  • Manage serverless APIs with Apache APISIX
    This article shows with the simple example how to manage Java-based serverless APIs build with Azure functions. It uses azure-functions plugin to integrate Apache APISIX API Gateway with Azure Serverless Function that invokes the HTTP trigger functions and return the response from Azure Cloud. - Source: dev.to / over 2 years ago
  • The Power of GitHub Actions for Streamlining DevOps Workflows
    GitHub Actions for Azure: This GitHub Action allows developers to automate tasks on the Microsoft Azure platform. With this action, developers can easily integrate Azure services into their workflows, such as Azure Functions, App Services, and Kubernetes. This can help streamline DevOps workflows by automating tasks such as deployment, testing, and scaling on the Azure platform. - Source: dev.to / over 2 years ago
  • NoOps: What Does the Future Hold for DevOps Engineers?
    NoOps is best suited for born-in-the-cloud environments that leverage PaaS and serverless solutions. Microservices and API-based application architectures fit the bill perfectly, as they offer fine-grained modularity along with automation. Leading cloud service providers like AWS, Azure, and GCP have a laser focus on providing more services and capabilities in PaaS and serverless, which would help accelerate the... - Source: dev.to / almost 4 years ago

Apache Airflow mentions (75)

  • The DOJ Still Wants Google to Sell Off Chrome
    Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / about 2 months ago
  • 10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
    Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 3 months ago
  • Data Orchestration Tool Analysis: Airflow, Dagster, Flyte
    Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / 3 months ago
  • AIOps, DevOps, MLOps, LLMOps – What’s the Difference?
    Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 4 months ago
  • Data Engineering with DLT and REST
    This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / 5 months ago
View more

What are some alternatives?

When comparing Azure Functions and Apache Airflow, you can also consider the following products

AWS Lambda - Automatic, event-driven compute service

Make.com - Tool for workflow automation (Former Integromat)

Google App Engine - A powerful platform to build web and mobile apps that scale automatically.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

Now Platform - Get native platform intelligence, so you can predict, prioritize, and proactively manage the work that matters most with the NOW Platform from ServiceNow.

Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.

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