Hadoop

Definition

Hadoop is an open-source software framework designed for distributed storage and processing of large data sets across clusters of computers using simple programming models. It allows for the handling of massive amounts of data in a cost-effective manner by utilizing commodity hardware, making it an essential tool in big data processing and cloud computing.

5 Must Know Facts For Your Next Test

1. Hadoop was created by Doug Cutting and Mike Cafarella in 2005 as a way to support the distribution and processing of large data sets for the Apache Nutch project.
2. It is highly scalable, meaning it can handle an increase in data volume by simply adding more nodes to the cluster without major changes to existing infrastructure.
3. Hadoop operates on a master-slave architecture where the master node manages the system and the slave nodes store and process the data.
4. The framework is designed to be fault-tolerant, automatically handling failures in hardware or software without losing data or disrupting processing.
5. Hadoop supports a wide variety of data formats including structured, semi-structured, and unstructured data, making it versatile for different types of big data applications.

Review Questions

• How does Hadoop's architecture facilitate the processing of big data across multiple machines?
  • Hadoop's architecture is built on a master-slave model where one master node controls the cluster and multiple slave nodes perform the actual data storage and processing. This allows tasks to be divided into smaller units that can be processed in parallel across different machines. This parallel processing capability significantly speeds up the computation time for large datasets, making Hadoop an efficient solution for handling big data.
• Discuss the role of HDFS in Hadoop's ability to manage large datasets efficiently.
  • The Hadoop Distributed File System (HDFS) plays a crucial role in managing large datasets by allowing them to be distributed across many machines. HDFS ensures that data is split into blocks and replicated across various nodes, which not only provides redundancy but also enables high throughput access to data. This design helps overcome limitations of traditional file systems, facilitating effective storage and retrieval of massive volumes of information.
• Evaluate the impact of Hadoop on cloud computing and its implications for big data analytics.
  • Hadoop has significantly transformed cloud computing by providing a framework that enables scalable storage and processing of big data in a cost-effective manner. Its ability to run on commodity hardware means organizations can leverage cloud resources more efficiently without the need for expensive infrastructure. The implications for big data analytics are profound, as Hadoop facilitates the analysis of vast datasets that were previously unmanageable, leading to improved insights and decision-making across industries.

Related terms

MapReduce: A programming model and processing engine that enables the distributed processing of large data sets by breaking tasks into smaller sub-tasks, which can be processed in parallel.

HDFS: The Hadoop Distributed File System, which is designed to store vast amounts of data across multiple machines while providing high throughput access to application data.

YARN: Yet Another Resource Negotiator, a resource management layer of Hadoop that allocates system resources and manages job scheduling for distributed applications.