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Spring Cloud Data Flow VS Apache Airflow

Compare Spring Cloud Data Flow VS Apache Airflow and see what are their differences

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

Spring Cloud Data Flow logo Spring Cloud Data Flow

Spring Cloud Data Flow is a platform capable of stream and batch data pipelines having the tools to create delicate topologies.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Spring Cloud Data Flow Landing page
    Landing page //
    2023-07-30
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Spring Cloud Data Flow features and specs

No features have been listed yet.

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.

Spring Cloud Data Flow videos

+ Add

Orchestrate All the Things! with Spring Cloud Data Flow - Eric Bottard & Ilayaperumal Gopinathan

More videos:

  • Review - Demo: Partitioning Batch jobs with Spring Cloud Data Flow & Task
  • Demo - 3 min demo: Spring Cloud Data Flow Metrics

Apache Airflow videos

+ Add

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Spring Cloud Data Flow and Apache Airflow)

Spring Cloud Data Flow

Apache Airflow

Big Data
100 100%
Big Data
0% 0
Workflow Automation
0 0%
Workflow Automation
100% 100
Stream Processing
100 100%
Stream Processing
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 Spring Cloud Data Flow and Apache Airflow

Spring Cloud Data Flow Reviews

We have no reviews of Spring Cloud Data Flow yet.
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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 Spring Cloud Data Flow. While we know about 75 links to Apache Airflow, we've tracked only 1 mention of Spring Cloud Data Flow. 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.

Spring Cloud Data Flow mentions (1)

  • Dataflow, a self-hosted Observable Notebook Editor
    And a Cloudera project: https://www.cloudera.com/products/cdf.html And an Azure feature: https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity And a Spring feature: https://spring.io/projects/spring-cloud-dataflow. - Source: Hacker News / 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 / 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 / 4 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 / 6 months ago
View more

What are some alternatives?

When comparing Spring Cloud Data Flow and Apache Airflow, you can also consider the following products

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

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

Spark Streaming - Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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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