Airflow vs Azure Functions

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

Azure Functions

Stacks785

Followers705

Votes62

Airflow vs Azure Functions: What are the differences?

1. Scalability and Ecosystem: Airflow is an open-source platform that provides a rich ecosystem of plugins and integrations, enabling users to extend and customize its functionality. In contrast, Azure Functions is a serverless compute service provided by Microsoft as part of the Azure cloud offering. Azure Functions benefits from the scalability and reliability of the underlying Azure infrastructure, making it ideal for event-driven scenarios and highly scalable workloads.
2. Language Support: Airflow is primarily built using Python, making it more suitable for python-centric workflows and tasks. On the other hand, Azure Functions supports multiple programming languages, including Python, JavaScript, C#, and PowerShell, offering more flexibility for developers to choose the language they are most comfortable with.
3. Orchestration Capabilities: Airflow is designed specifically for workflow orchestration, providing a wide range of features such as task dependency management, scheduling, and monitoring. It allows users to define complex workflows and dependencies between tasks. In contrast, while Azure Functions can be used for orchestrating workflows, it is primarily designed for executing individual functions in a serverless manner, rather than managing the overall workflow.
4. Infrastructure Management: Airflow requires users to set up and manage their infrastructure, including servers, databases, and the Airflow scheduler. Users need to ensure scalability, availability, and resilience of the infrastructure to support Airflow's operation. Conversely, Azure Functions abstracts away the infrastructure management by leveraging the Azure platform, allowing users to focus solely on writing code and deploying functions.
5. Triggers and Event Processing: Airflow supports various types of triggers such as time-based scheduling, manual triggering, and event-based triggering. It offers a wide range of built-in operators and sensors to process events and execute tasks accordingly. On the other hand, Azure Functions is specifically designed for event-driven scenarios and provides extensive support for event-based triggers such as HTTP requests, database changes, and queue messages.
6. Consumption-based Pricing Model: Airflow operates on a typical deployment model where users are responsible for provisioning and managing the resources required to run Airflow. In contrast, Azure Functions follows a consumption-based pricing model, where users only pay for the actual execution time and resources consumed by their functions, making it more cost-effective for scenarios with varying workload demands.

In Summary, Airflow provides a rich ecosystem for workflow orchestration, while Azure Functions offers scalability, multi-language support, event-driven architecture, and a consumption-based pricing model.

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Advice on Airflow, Azure Functions

Mark

Nov 2, 2020

Needs adviceon

Microsoft Azure

Need advice on what platform, systems and tools to use.

Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.

We are open to either build it on AWS or on Microsoft Azure.

Apologies if I'm leaving out some info. My first post. :) Thanks in advance!

133k views133k

Comments

Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

294k views294k

Comments

Detailed Comparison

Airflow	Azure Functions
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.	Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.	Easily schedule event-driven tasks across services;Expose Functions as HTTP API endpoints;Scale Functions based on customer demand;Develop how you want, using a browser-based UI or existing tools;Get continuous deployment, remote debugging, and authentication out of the box
Statistics
Stacks 1.7K	Stacks 785
Followers 2.8K	Followers 705
Votes 128	Votes 62
Pros & Cons
Pros 53 Features 14 Task Dependency Management 12 Cluster of workers 12 Beautiful UI 10 Extensibility Cons 2 Running it on kubernetes cluster relatively complex 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 1 Logical separation of DAGs is not straight forward	Pros 14 Pay only when invoked 11 Great developer experience for C# 9 Multiple languages supported 7 Great debugging support 5 Can be used as lightweight https service Cons 1 Sporadic server & language runtime issues 1 No persistent (writable) file system available 1 Not suited for long-running applications 1 Poor support for Linux environments
Integrations
No integrations available	Azure DevOps Java Bitbucket Node.js Microsoft Azure GitHub Visual Studio Code JavaScript Azure Cosmos DB C#

What are some alternatives to Airflow, Azure Functions?

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Related Comparisons

Airflow vs Azure Functions: What are the differences?

1. Scalability and Ecosystem: Airflow is an open-source platform that provides a rich ecosystem of plugins and integrations, enabling users to extend and customize its functionality. In contrast, Azure Functions is a serverless compute service provided by Microsoft as part of the Azure cloud offering. Azure Functions benefits from the scalability and reliability of the underlying Azure infrastructure, making it ideal for event-driven scenarios and highly scalable workloads.
2. Language Support: Airflow is primarily built using Python, making it more suitable for python-centric workflows and tasks. On the other hand, Azure Functions supports multiple programming languages, including Python, JavaScript, C#, and PowerShell, offering more flexibility for developers to choose the language they are most comfortable with.
3. Orchestration Capabilities: Airflow is designed specifically for workflow orchestration, providing a wide range of features such as task dependency management, scheduling, and monitoring. It allows users to define complex workflows and dependencies between tasks. In contrast, while Azure Functions can be used for orchestrating workflows, it is primarily designed for executing individual functions in a serverless manner, rather than managing the overall workflow.
4. Infrastructure Management: Airflow requires users to set up and manage their infrastructure, including servers, databases, and the Airflow scheduler. Users need to ensure scalability, availability, and resilience of the infrastructure to support Airflow's operation. Conversely, Azure Functions abstracts away the infrastructure management by leveraging the Azure platform, allowing users to focus solely on writing code and deploying functions.
5. Triggers and Event Processing: Airflow supports various types of triggers such as time-based scheduling, manual triggering, and event-based triggering. It offers a wide range of built-in operators and sensors to process events and execute tasks accordingly. On the other hand, Azure Functions is specifically designed for event-driven scenarios and provides extensive support for event-based triggers such as HTTP requests, database changes, and queue messages.
6. Consumption-based Pricing Model: Airflow operates on a typical deployment model where users are responsible for provisioning and managing the resources required to run Airflow. In contrast, Azure Functions follows a consumption-based pricing model, where users only pay for the actual execution time and resources consumed by their functions, making it more cost-effective for scenarios with varying workload demands.

Airflow vs Azure Functions

Overview