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The Apache Software Foundation

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4,401 reviews
  • 198 profiles
  • 100 categories
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4.2
#1 in 2 categories
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Serving customers since
1999

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Apache HTTP Server Project

146 reviews

The Apache HTTP Server, commonly known as Apache, is a free and open-source web server software that has been a cornerstone of the internet since its inception in 1995. Developed and maintained by the Apache Software Foundation, Apache is designed to provide a secure, efficient, and extensible server that adheres to current HTTP standards. Its modular architecture allows for a wide range of functionalities, making it suitable for various web hosting needs, from serving static content to handling dynamic web applications. Key Features and Functionality: - Modular Architecture: Apache's design enables the addition of modules to extend its capabilities, such as authentication, URL rewriting, and proxying. - Cross-Platform Compatibility: It operates on numerous operating systems, including UNIX-like systems and Windows, ensuring flexibility across different environments. - Support for Multiple Protocols: Apache supports HTTP/1.1 and HTTP/2 protocols, facilitating efficient communication between clients and servers. - Security Features: With modules like mod_ssl, Apache provides robust support for TLS/SSL encryption, ensuring secure data transmission. - Virtual Hosting: This feature allows a single Apache instance to serve multiple websites, each with its own domain and configuration. - Integration with Programming Languages: Apache seamlessly integrates with languages such as PHP, Python, and Perl, enabling dynamic content generation. - Comprehensive Logging and Monitoring: It offers detailed logging capabilities, aiding in monitoring server performance and diagnosing issues. Primary Value and User Solutions: Apache's primary value lies in its reliability, flexibility, and extensive community support. It addresses the needs of web administrators and developers by providing a customizable and scalable platform for hosting websites and applications. Its open-source nature ensures continuous improvement and adaptation to emerging web technologies, making it a preferred choice for both small-scale personal projects and large-scale enterprise deployments.

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Apache Maven

168 reviews

Apache Maven is a robust build automation and project management tool primarily designed for Java projects, though it also supports languages like C#, Ruby, and Scala. Developed by the Apache Software Foundation, Maven streamlines the software development process by managing project builds, dependencies, and documentation through a standardized approach. Key Features and Functionality: - Project Object Model (POM): Centralizes project configuration in a single XML file, detailing project information, dependencies, and build instructions. - Dependency Management: Automatically handles project dependencies by downloading required libraries from repositories and managing versioning to ensure compatibility. - Build Lifecycle: Defines a series of build phases (e.g., compile, test, package) that standardize the build process, ensuring consistency across projects. - Plugin Architecture: Extensible through a wide range of plugins that add functionalities such as code compilation, testing, and packaging. - Convention over Configuration: Emphasizes predefined conventions to reduce the need for extensive configuration, allowing developers to focus on coding rather than build setup. Primary Value and Problem Solving: Maven addresses the complexities of modern software development by providing a uniform build system that simplifies project setup and maintenance. By automating dependency management, it eliminates the manual tracking of libraries and their versions, reducing the risk of conflicts and errors. Its standardized build lifecycle ensures that projects are built, tested, and packaged consistently, enhancing reliability and facilitating collaboration among development teams. Additionally, Maven's extensible plugin system allows for customization to meet specific project needs, making it a versatile tool in the software development lifecycle.

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Apache Open Climate Workbench

4 reviews

Apache Open Climate Workbench (OCW) is an open source software development project geared towards enabling climate model analysis and evaluation.

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Apache Eagle

4 reviews

Apache Eagle is an open source analytics solution for identifying security and performance issues instantly on big data platforms, e.g. Apache Hadoop, Apache Spark etc. It analyzes data activities, yarn applications, jmx metrics, and daemon logs etc.and provides state-of-the-art alert engine to identify security breach, performance issues and shows insights.

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Apache log4cxx

4 reviews

Apache log4cxx is a logging framework for C++ patterned which uses Apache Portable Runtime for most platform-specific code and should be usable on any platform supported by APR.

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Apache ACE

3 reviews

Apache ACE is a software distribution framework designed to allow users to centrally manage and distribute software components, configuration data and other artifacts to target systems.

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Apache Bloodhound

3 reviews

Apache Bloodhound is an open source web-based project management and bug tracking system.

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Apache AntUnit

3 reviews

Apache AntUnit is an Antlib that provides a test framework for Apache Ant tasks and types.

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Apache Anakia

3 reviews

Apache Anakia is an XML transformation tool which uses JDOM and Velocity to transform XML documents into the format of your choice.

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Apache Chemistry

3 reviews

Apache Chemistry provides open source implementations of the Content Management Interoperability Services (CMIS) specification.

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Apache Rivet

3 reviews

Apache Rivet is a powerful, flexible, consistent, fast, and robust solution to creating web applications.

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The Apache Software Foundation Reviews

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31
Yogesh S.
YS
Yogesh S.
Software Engineer | Backend & Cloud Developer | Java • Spring Boot • AWS | Building Scalable Enterprise Applications
06/13/2026
Validated Reviewer
Review source: G2 invite
Incentivized Review

Reliable and lightweight, but definitely shows its age

Our engineering team primarily uses Tomcat to host several of our older internal Java web applications and a few core REST APIs that feed data to our main administrative dashboards. It typically sits behind an Nginx reverse proxy in our production environment, serving as the dependable workhorse for our servlet and JSP-based projects.The absolute best thing about Tomcat is that it just quietly does its job without demanding a massive server footprint. Compared to full-blown enterprise application servers, it boots up incredibly fast and is highly focused on doing exactly what it was designed to do. Because it has been an industry standard for so long, the community support is basically unmatched. If you run into a weird deployment error or a specific thread-hanging issue, chances are high that someone else solved that exact same problem on a forum ten years ago. Furthermore, being completely free and open-source means we can spin up as many instances as we need across our testing and staging environments without ever worrying about complex licensing roadblocks.
Sachin G.
SG
Sachin G.
Machine Learning Engineer | AI & Predictive Analytics Specialist | Python, TensorFlow, NLP & Cloud ML | Building Scalable Intelligent Systems
06/10/2026
Validated Reviewer
Review source: G2 invite
Incentivized Review

Powerful for complex ML pipelines, but comes with a steep infrastructure learning curve

We use Apache Airflow as the central orchestrator for our entire machine learning and data engineering lifecycle. Specifically, it manages the schedules and dependencies for pulling raw data from our production databases, orchestrating daily feature engineering jobs, and kicking off automated model retraining pipelines. Once the models are evaluated, Airflow also triggers the deployment scripts to push updated model artifacts to our staging environment. It basically acts as the glue holding our data workflows together, ensuring that everything runs in the exact sequence required.What I appreciate most about Airflow is its "configuration as code" philosophy. Because every workflow is defined as a Python script, it fits perfectly into our existing development practices. We can version control our DAGs in Git, run code reviews on them, and easily write custom operators when the built-in ones don't quite fit our needs. The UI is also incredibly detailed; when a complex pipeline fails at 3:00 AM, the tree view and the ability to dig directly into the logs of a specific failed task save us an immense amount of troubleshooting time. The open-source community is massive, so if you are trying to connect Airflow to a database or a cloud service, chances are a robust provider package already exists.
Lokesh S.
LS
Lokesh S.
Results-Driven Data Scientist | Machine Learning | Predictive Analytics | Python | SQL | Power BI | NLP | AI Solutions for Business Growth
06/03/2026
Validated Reviewer
Review source: G2 invite
Incentivized Review

Incredible flexibility with Python, but requires dedicated maintenance

Our data pipelines, and all machine learning workflows are orchestrated on top of Apache Airflow. We have a variety of ETL jobs running on a schedule and some more complicated multi-step ML training loops. Airflow is in the middle, consuming data from a number of production databases, running our data preprocessing scripts, feeding our modelling training pipelines, and sending the results to our data analytics dashboards and cloud storage. It's like the clock and the nervous system within our whole data architecture design.That's definitely the selling point of Airflow that everyone can't outrace from - configuration as code. Being completely in Python it gives us a lot of flexibility. I can work on real data workflows using common software engineering techniques such as git version control, code reviews, and unit-testing my code. The UI is also great for debugging: I'm sure someone is running a complex pipeline somewhere, at 3:00 AM some of their critical tasks are failing, and using the tree view and direct access to the task logs, I can be almost certain which task failed and why. Oh, and, because they have such a big community, we have an operator or a provider for almost any cloud service or tool we want to connect to.

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What is The Apache Software Foundation?

Community-led development since 1999. FoundationProjectsPeopleGet InvolvedDownloadSupport ApacheHome. We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

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Year Founded
1999
Website
knox.apache.org