Distributed computing

Definition

Distributed computing refers to a model where computing resources, such as processors and memory, are spread across multiple locations and connected through a network to work on a common task. This approach enables systems to share workloads, enhancing performance, fault tolerance, and scalability, especially important when handling big data. By distributing tasks among various nodes, it allows for efficient processing of large datasets that single machines would struggle to manage.

5 Must Know Facts For Your Next Test

1. Distributed computing enhances scalability by allowing systems to grow easily, adding more nodes to handle increasing amounts of data and computation.
2. It improves fault tolerance because if one node fails, others can take over its tasks without disrupting the overall system.
3. This model is crucial for big data applications as it enables efficient processing of massive datasets by breaking them into smaller chunks processed in parallel.
4. Distributed computing can reduce latency by placing resources closer to the users, leading to faster data access and processing times.
5. Technologies like Hadoop and Spark leverage distributed computing principles to process large-scale data efficiently across clusters of computers.

Review Questions

• How does distributed computing improve scalability in handling large datasets?
  • Distributed computing improves scalability by enabling systems to add more nodes or machines as the workload increases. This allows for the division of tasks into smaller units that can be processed in parallel, effectively managing larger datasets than a single machine could handle. As demand grows, additional resources can be allocated easily without significant redesign of the existing architecture.
• In what ways does distributed computing enhance fault tolerance compared to traditional computing methods?
  • Distributed computing enhances fault tolerance by ensuring that the failure of one node doesn't bring down the entire system. Tasks are spread across multiple machines, so if one fails, others can take over its responsibilities without affecting overall performance. This redundancy means systems can continue functioning smoothly, maintaining service availability even in the face of hardware or software failures.
• Evaluate the impact of distributed computing on big data analytics and provide examples of its application.
  • Distributed computing has revolutionized big data analytics by allowing for the processing of massive datasets across multiple machines in parallel. This approach leads to faster analysis and insights, which are essential in fields like finance, healthcare, and social media. For example, platforms like Apache Hadoop utilize distributed computing principles to store and analyze large volumes of data effectively, while Apache Spark provides real-time processing capabilities that harness distributed resources for immediate insights.

Related terms

Parallel Computing: A computing paradigm where multiple calculations or processes are carried out simultaneously, often used in conjunction with distributed computing to speed up processing tasks.

Cloud Computing: A model that provides on-demand access to shared computing resources over the internet, often utilizing distributed computing to enhance scalability and resource management.

Cluster Computing: A type of distributed computing where a group of linked computers work together as a single system to perform tasks more efficiently.