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Apache Apex VS Hadoop

Compare Apache Apex VS Hadoop and see what are their differences

Apache Apex logo Apache Apex

Apache Apex is an enterprise-grade unified stream and batch processing engine.

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • Apache Apex Landing page
    Landing page //
    2021-09-30
  • Hadoop Landing page
    Landing page //
    2021-09-17

Apache Apex features and specs

No features have been listed yet.

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

Apache Apex videos

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Hadoop videos

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

Category Popularity

0-100% (relative to Apache Apex and Hadoop)
Big Data
18 18%
82% 82
Databases
0 0%
100% 100
Data Warehousing
100 100%
0% 0
Data Dashboard
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Apex and Hadoop

Apache Apex Reviews

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Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...

Social recommendations and mentions

Based on our record, Hadoop seems to be a lot more popular than Apache Apex. While we know about 23 links to Hadoop, we've tracked only 1 mention of Apache Apex. 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.

Apache Apex mentions (1)

  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago

Hadoop mentions (23)

  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / 5 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / about 2 months ago
  • Apache Hadoop: Pioneering Open Source Innovation in Big Data
    Apache Hadoop is more than just software—it’s a full-fledged ecosystem built on the principles of open collaboration and decentralized governance. Born out of a need to process vast amounts of information efficiently, Hadoop uses a distributed file system and the MapReduce programming model to enable scalable, fault-tolerant computing. Central to its success is a diverse ecosystem that includes influential... - Source: dev.to / 2 months ago
  • Embracing the Future: India's Pioneering Journey in Open Source Development
    Navya: Designed to streamline administrative processes in educational institutions, Navya continues to demonstrate the power of open source in addressing local needs. Additionally, India’s vibrant tech communities are well represented on platforms like GitHub and SourceForge. These platforms host numerous Indian-led projects and serve as collaborative hubs for developers across diverse technology landscapes.... - Source: dev.to / 2 months ago
  • Where is Java Used in Industry?
    The rise of big data has seen Java arise as a crucial player in this domain. Tools like Hadoop and Apache Spark are built using Java, enabling businesses to process and analyze massive datasets efficiently. Java’s scalability and performance are critical for big data results that demand high trustability. - Source: dev.to / 5 months ago
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What are some alternatives?

When comparing Apache Apex and Hadoop, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Snowplow - Snowplow is an enterprise-strength event analytics platform.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.