Hadoop

Definition

Hadoop is an open-source framework designed for distributed storage and processing of large data sets across clusters of computers using simple programming models. It utilizes a master-slave architecture, where the master node coordinates the distribution of tasks while slave nodes perform the actual computation and data storage. This setup is especially useful for handling vast amounts of unstructured data, making it a core technology in big data analytics.

5 Must Know Facts For Your Next Test

1. Hadoop allows for the storage of massive amounts of data on commodity hardware, which significantly reduces costs compared to traditional systems.
2. The framework's scalability enables it to handle petabytes of data seamlessly by simply adding more nodes to the cluster.
3. Hadoop is fault-tolerant, meaning if a node fails during processing, the system automatically reassigns tasks to other available nodes without losing data.
4. The ecosystem includes various tools and components like Hive, Pig, and HBase that enhance Hadoop's capabilities for querying and processing data.
5. Hadoop supports both structured and unstructured data formats, making it versatile for different types of big data applications.

Review Questions

• How does Hadoop's master-slave architecture enhance its capability to process large data sets?
  • Hadoop's master-slave architecture improves its ability to process large data sets by dividing tasks between a master node and multiple slave nodes. The master node is responsible for managing job scheduling and resource allocation, while slave nodes execute the actual data processing. This parallel processing allows Hadoop to handle vast volumes of data efficiently, as each slave can work on different parts of the dataset simultaneously, leading to faster computation times.
• Discuss the role of MapReduce in Hadoop's processing model and how it interacts with HDFS.
  • MapReduce plays a crucial role in Hadoop's processing model by allowing developers to write applications that process large datasets in parallel. It consists of two main functions: 'Map', which filters and sorts input data, and 'Reduce', which aggregates the results. HDFS stores these large datasets across different nodes in the cluster, ensuring that MapReduce jobs can access data locally on each node, minimizing network congestion and increasing efficiency during processing.
• Evaluate how Hadoop's features contribute to its application in big data analytics compared to traditional database systems.
  • Hadoop's features significantly enhance its application in big data analytics when compared to traditional database systems. Its ability to store vast amounts of unstructured data on commodity hardware allows organizations to scale their storage needs cost-effectively. Additionally, Hadoop's fault tolerance ensures uninterrupted processing even when hardware failures occur. The ecosystem around Hadoop, with tools like Hive and Pig, further enables complex analytical queries and data manipulation, making it more suited for dynamic and diverse datasets than rigid schema-based traditional databases.

Related terms

MapReduce: A programming model used for processing large data sets with a distributed algorithm on a cluster, breaking the tasks into smaller sub-tasks that can be processed in parallel.

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

YARN: Yet Another Resource Negotiator, a resource management layer for Hadoop that manages and schedules resources across the cluster for various applications.