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  5. Airflow vs Google Cloud Dataflow

Airflow vs Google Cloud Dataflow

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Google Cloud Dataflow
Google Cloud Dataflow
Stacks219
Followers497
Votes19

Airflow vs Google Cloud Dataflow: What are the differences?

Airflow and Google Cloud Dataflow are both popular tools for data processing and workflow management. Let's explore the key differences between the two:

  1. Execution Model: Airflow is based on a Directed Acyclic Graph (DAG) model, where users define workflows as a series of tasks and dependencies. Each task is independent and can run on any machine, making it easier to distribute workloads across multiple machines. On the other hand, Google Cloud Dataflow uses a data parallel model, where data is divided into chunks and processed in parallel across a distributed system. This makes it well-suited for large-scale computations and data processing.

  2. Scalability: While both Airflow and Google Cloud Dataflow can scale horizontally to handle increasing workloads, they have different approaches to achieving scalability. Airflow relies on task parallelism, where multiple tasks can be executed simultaneously, while Google Cloud Dataflow leverages data parallelism, which allows processing multiple chunks of data in parallel. This makes Google Cloud Dataflow highly scalable for processing large datasets.

  3. Integration with Cloud Services: Google Cloud Dataflow is tightly integrated with Google Cloud Platform (GCP). It can seamlessly process data from various GCP services like BigQuery, Cloud Storage, and Pub/Sub. It also provides connectors for other cloud and on-premises data sources. On the other hand, Airflow is a more agnostic tool and can integrate with a wide range of services and platforms, including cloud providers like AWS and Azure.

  4. Programming Language Support: Airflow supports a wide range of programming languages, including Python, Java, and SQL, allowing users to write custom functions and tasks in their language of choice. Google Cloud Dataflow primarily supports Java and Python, with limited support for other languages. This difference in language support may influence the choice of tool based on the programming language preferences of the team.

  5. Data Processing Models: Airflow primarily focuses on task orchestration and workflow management, where each task represents a discrete unit of work. It provides a rich set of operators for data ingestion, transformation, and analysis. Google Cloud Dataflow, on the other hand, is specifically designed for large-scale data processing and analytics. It provides advanced data processing capabilities like windowing, streaming, and stateful processing, which may be critical for certain use cases.

  6. Ease of Use and Learning Curve: Airflow offers a web-based UI and a user-friendly interface for creating and managing workflows. It has a relatively shallow learning curve and is easy to use for developers, data engineers, and data scientists. Google Cloud Dataflow, on the other hand, has a steeper learning curve due to its distributed processing nature and the need to write code using the Dataflow SDK. It may require more technical expertise to fully utilize its capabilities.

In summary, Airflow and Google Cloud Dataflow differ in their execution models, scalability approaches, integration with cloud services, programming language support, data processing capabilities, and ease of use.

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Advice on Airflow, Google Cloud Dataflow

Anonymous
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.

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Comments

Detailed Comparison

Airflow
Airflow
Google Cloud Dataflow
Google Cloud Dataflow

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.

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

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.
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks
1.7K
Stacks
219
Followers
2.8K
Followers
497
Votes
128
Votes
19
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
Pros
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency

What are some alternatives to Airflow, Google Cloud Dataflow?

GitHub Actions

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.

Apache Beam

Apache Beam

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

Zenaton

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.

Amazon Kinesis

Amazon Kinesis

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

Luigi

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.

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

Shipyard

na

PromptX

PromptX

PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.

iLeap

iLeap

ILeap is a low-code app development platform to build custom apps and automate workflows visually, helping you speed up digital transformation.

AI Autopilot

AI Autopilot

Agentic AI Platform for Intelligent IT Automation built by MSPs for MSPs. Revolutionize your operations with advanced AI agents.

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