apache/hadoop
Hadoop
Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster.
The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big data storage and cluster computing.
The system covers large scale data processing and big data infrastructure management. It provides capabilities for distributing data across clusters and executing computational tasks across multiple nodes to handle volumes of information too large for a single computer.
Features
- Big Data Processing - Provides the primary infrastructure for managing, storing, and processing massive volumes of data across distributed systems.
- Distributed Computing - Executes large-scale data analytics and processing tasks in parallel across distributed computing clusters.
- Distributed Data Processing Frameworks - Provides a framework for partitioning, transforming, and processing large-scale datasets across distributed clusters.
- Distributed File Systems - Implements a scalable distributed file system that splits files into blocks across commodity hardware nodes.
- Large-Scale Data Computation - Implements a distributed framework for executing complex data analysis and computation across large clusters.
- MapReduce Processing Engines - Provides a parallel computing engine based on the MapReduce programming model for processing massive datasets.
- Distributed Storage Clusters - Creates a scalable storage system by aggregating multiple nodes into a unified distributed storage cluster.
- Distributed File Systems - Implements a scalable distributed file system that manages large files across multiple nodes for fault tolerance.
- Data-Locality Scheduling - Implements scheduling that minimizes network traffic by executing logic on the physical node where the required data resides.
- Fault Tolerance - Ensures high data availability and resilience by replicating data blocks across multiple physical nodes.
- Cluster Resource Managers - Provides a mechanism to allocate computing resources and schedule jobs across distributed network nodes to optimize hardware usage.
- Master-Worker Coordination - Uses a central node to manage metadata and orchestrate task assignment to worker nodes across the cluster.
- Big Data Frameworks - Framework for distributed processing of large datasets.
- Data Processing - Distributed processing framework for big data workloads.
- Data Processing and Analysis - Foundation for distributed storage and large-scale data processing.
- Distributed Filesystems - Distributed filesystem for high-throughput application data.
- Data Engineering - Framework for distributed processing of large datasets.
- Data Infrastructure Management - Framework for distributed processing of large datasets across compute clusters.
