Announcement
Apache Hadoop - Open-Source Framework for Distributed Big Data Storage & Processing
Apache Hadoop is an open-source framework for distributed storage and distributed processing of very large data sets 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 rely on hardware to deliver high-availability, the framework itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Project Introduction
Summary
Apache Hadoop is a foundational open-source software framework that enables distributed storage and processing of large datasets using the MapReduce programming model. It is a cornerstone technology for big data analytics.
Problem Solved
Traditional computing systems struggle to process and store the ever-increasing volume, velocity, and variety of big data. Hadoop provides a scalable and fault-tolerant solution to handle massive datasets across commodity hardware.
Core Features
Hadoop Distributed File System (HDFS)
Allows for storing large data sets across multiple machines, providing high availability and fault tolerance.
MapReduce
A programming model for processing large data sets with a parallel, distributed algorithm on a cluster.
YARN
Yet Another Resource Negotiator is the resource management layer of Hadoop, allocating computing resources and scheduling user applications.
Tech Stack
使用场景
Apache Hadoop is leveraged in various industries and scenarios that involve handling and processing large volumes of data, including:
Log Processing and Analysis
Details
Processing and analyzing large volumes of log files from web servers, applications, and systems for monitoring, security, and usage analytics.
User Value
Enables scalable processing of petabytes of log data, providing deep insights into system behavior and user activity.
Enterprise Data Warehousing / Data Lake
Details
Building large-scale data warehouses and data lakes for storing and querying structured and unstructured data from various sources.
User Value
Provides a cost-effective and scalable platform for centralizing diverse data assets for enterprise-wide analytics.
Machine Learning Data Preparation
Details
Preparing and processing massive datasets required for training machine learning models, especially for complex tasks like image or natural language processing.
User Value
Facilitates the handling of training data volumes that exceed the capacity of traditional systems, accelerating AI/ML development.
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