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Airflow vs Azure Data Factory: What are the differences?

Introduction:

In this article, we will discuss the key differences between Airflow and Azure Data Factory. Both Airflow and Azure Data Factory are popular platforms used for orchestrating and managing workflows in data pipelines or ETL (Extract, Transform, Load) processes. However, there are significant differences between these two platforms in terms of architecture, deployment options, ecosystem, and capabilities.

1. Architecture:

Airflow is based on a Directed Acyclic Graph (DAG) model, where tasks are represented as nodes and dependencies between the tasks are represented as edges. It has a centralized scheduler and uses a relational database as a backend. On the other hand, Azure Data Factory follows a pipeline-centric model, with support for data movement, data transformation, and data monitoring activities. It is cloud-native and is built on a serverless architecture using services like Azure Functions and Azure Logic Apps.

2. Deployment Options:

Airflow can be deployed on-premises or in the cloud. It provides flexibility in terms of deployment options, allowing users to choose the infrastructure and environment they prefer. Azure Data Factory, on the other hand, is a cloud-based service and can only be deployed on the Azure cloud platform. It leverages the infrastructure provided by Azure and eliminates the need for managing and maintaining the underlying infrastructure.

3. Ecosystem:

Airflow has a mature and active open-source community with a wide range of third-party integrations and contributions. It supports various databases, message brokers, and executors, allowing users to choose the technologies that best suit their needs. Azure Data Factory, being a product of Microsoft, has a rich ecosystem of Azure services and integrations. It seamlessly integrates with other Azure services like Azure Data Lake Storage, Azure Blob Storage, Azure Synapse Analytics, and more.

4. Scalability:

Airflow can be scaled horizontally by adding more worker nodes as the workload increases. However, it requires manual configuration and management of these worker nodes. Azure Data Factory, being a cloud-based service, offers built-in scalability and can automatically scale up or down based on the workload. It leverages the scalability and elasticity of Azure services, ensuring optimal resource utilization and cost efficiency.

5. Monitoring and Alerting:

Airflow provides a web-based user interface for monitoring and managing workflows. It also supports integration with external monitoring tools like Grafana and Prometheus for advanced monitoring and alerting capabilities. Azure Data Factory provides a rich set of monitoring and logging capabilities out of the box. It integrates with Azure Monitor and Azure Log Analytics, offering real-time monitoring, alerting, and diagnostics for pipelines and activities.

6. Pricing Model:

Airflow has an open-source version available for free, but it requires infrastructure and resources for deployment and maintenance. It offers flexibility in terms of infrastructure choices, but users need to consider and manage the associated costs. Azure Data Factory follows a pay-as-you-go pricing model, where users pay for the resources and services consumed. It offers different pricing tiers based on the required features and capabilities, allowing users to choose the most cost-effective option.

In Summary, Airflow and Azure Data Factory differ in architecture, deployment options, ecosystem, scalability, monitoring capabilities, and pricing model. Airflow offers flexibility, while Azure Data Factory provides ease of deployment and integration with the Azure ecosystem. Choosing between them depends on specific requirements, infrastructure preferences, and budget considerations.

Advice on Airflow and Azure Data Factory
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 255.5K views

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Apache SparkApache Spark

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.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 275.9K views
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on
CassandraCassandra

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Azure Data Factory
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
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    Cons of Airflow
    Cons of Azure Data Factory
    • 2
      Observability is not great when the DAGs exceed 250
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
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      - No public GitHub repository available -

      What is Airflow?

      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.

      What is Azure Data Factory?

      It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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      What companies use Airflow?
      What companies use Azure Data Factory?
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      What are some alternatives to Airflow and Azure Data Factory?
      Luigi
      It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
      Apache NiFi
      An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
      Jenkins
      In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
      AWS Step Functions
      AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
      Pachyderm
      Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
      See all alternatives