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GlusterFS VS Apache Airflow

Compare GlusterFS VS Apache Airflow and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

GlusterFS logo GlusterFS

GlusterFS is a scale-out network-attached storage file system.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • GlusterFS Landing page
    Landing page //
    2019-03-10
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

GlusterFS features and specs

  • Scalability
    GlusterFS can easily scale out by adding more servers to the cluster, allowing it to handle increasing amounts of data and traffic.
  • Distributed File System
    It provides a distributed file system, enabling data replication and distribution across multiple nodes, which enhances data availability and reliability.
  • Open Source
    Being open source, GlusterFS provides flexibility and freedom for customization to fit specific needs without the cost associated with proprietary solutions.
  • POSIX Compliance
    GlusterFS is POSIX-compliant, meaning it supports standard file system operations, which makes it easier to integrate with existing applications and systems.
  • High Availability
    With built-in features like self-healing and replication, GlusterFS ensures that data remains available and consistent even in the event of hardware failures.
  • Geographical Distribution
    It supports geographical distribution of data, which is beneficial for disaster recovery and accessing data from multiple locations.

Possible disadvantages of GlusterFS

  • Performance Overhead
    Due to its distributed nature, GlusterFS might introduce performance overhead, particularly for workloads requiring low-latency or high-throughput.
  • Complexity in Management
    Managing a GlusterFS cluster can be complex, requiring in-depth knowledge of the system to properly configure and troubleshoot issues.
  • Latency Issues
    Latency can become a significant issue, especially in write-heavy applications or when nodes are geographically distant.
  • Resource Intensive
    GlusterFS can be resource-intensive, requiring significant CPU and memory resources to manage its distributed architecture and ensure data consistency.
  • Lack of Advanced Features
    Compared to other distributed file systems, GlusterFS may lack some advanced features like native support for certain storage protocols or comprehensive storage tiering.
  • Community Support
    While there is a community around GlusterFS, the level and speed of community support may not match that of commercially-backed solutions.

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.

Analysis of Apache Airflow

Overall verdict

  • Yes, Apache Airflow is a good choice for managing complex workflows and data pipelines, particularly for organizations that require a scalable and reliable orchestration tool.

Why this product is good

  • Apache Airflow is considered good because it provides a robust and flexible platform for authoring, scheduling, and monitoring workflows. It is open-source and has a large community that contributes to its continuous improvement. Airflow's modular architecture allows for easy integration with various data sources and destinations, and its UI is user-friendly, enabling effective pipeline visualization and management. Additionally, it offers extensibility through a wide array of plugins and customization options.

Recommended for

    Apache Airflow is recommended for data engineers, data scientists, and IT professionals who need to automate and manage workflows. It is particularly suited for organizations handling large-scale data processing tasks, requiring integration with various systems, and those looking to deploy machine learning pipelines or ETL processes.

GlusterFS videos

+ Add

An Overview of GlusterFS Architecture Part 2 - Non-replicated Cluster

Apache Airflow videos

+ Add

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to GlusterFS and Apache Airflow)

GlusterFS

Apache Airflow

Cloud Storage
100 100%
Cloud Storage
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 GlusterFS and Apache Airflow

GlusterFS Reviews

We have no reviews of GlusterFS 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 GlusterFS. While we know about 75 links to Apache Airflow, we've tracked only 2 mentions of GlusterFS. 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.

GlusterFS mentions (2)

  • [D] What are the compute options you've considered for your projects?
    I am a fan of Gearman to schedule and dispatch distributed jobs, Redis as a collaborative blackboard, and GlusterFS to share models across multiple systems and make bulk data available across the entire system (usually referenced in the blackboard as a pathname). Source: about 2 years ago
  • Gluster vs Oracle Gluster
    If you're not relying on support, then I would probably standardize on the latest packages available from gluster.org. Source: 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 / 3 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 / 4 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 / 5 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 GlusterFS and Apache Airflow, you can also consider the following products

Ceph - Ceph is a distributed object store and file system designed to provide excellent performance...

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

rkt - App Container runtime

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

Apache Karaf - Apache Karaf is a lightweight, modern and polymorphic container powered by OSGi.

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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