Distributed computing

Definition

Distributed computing is a model where multiple computer systems work together to complete tasks, sharing resources and processing power across a network. This approach allows for greater scalability, fault tolerance, and efficiency by breaking down complex problems into smaller, manageable pieces that can be processed simultaneously. In the context of data processing frameworks, it enables large-scale data analysis and manipulation across clusters of machines.

5 Must Know Facts For Your Next Test

1. Distributed computing allows for the processing of large datasets that exceed the capabilities of a single machine, making it essential for big data applications.
2. Hadoop and Spark are two popular frameworks that utilize distributed computing to handle massive amounts of data across clusters effectively.
3. One key advantage of distributed computing is fault tolerance; if one node fails, others can continue processing without losing overall progress.
4. Data locality is an important concept in distributed computing, as moving data to where processing occurs reduces network latency and speeds up tasks.
5. In distributed computing, resource management and task scheduling are crucial for optimizing performance and ensuring efficient use of hardware resources.

Review Questions

• How does distributed computing enhance the efficiency of data processing compared to traditional single-machine computing?
  • Distributed computing enhances efficiency by breaking down large tasks into smaller sub-tasks that can be processed concurrently across multiple machines. This parallel processing capability allows for quicker execution times and better resource utilization, as each machine can handle different parts of the workload simultaneously. Traditional single-machine computing often struggles with large datasets due to memory and processing limitations, while distributed systems can scale out by adding more machines.
• Discuss the role of Hadoop and Spark in implementing distributed computing and how they differ in their approach.
  • Hadoop and Spark both enable distributed computing but have different architectures and approaches. Hadoop uses the MapReduce programming model, which processes data in two phases: mapping and reducing. It is designed for batch processing and is efficient for handling large volumes of data stored in Hadoop Distributed File System (HDFS). In contrast, Spark offers in-memory processing capabilities, allowing it to handle real-time data analytics and iterative algorithms much faster than Hadoop. Spark can also work with various data sources beyond HDFS, making it more versatile.
• Evaluate the impact of distributed computing on modern data science practices, particularly regarding scalability and collaboration.
  • Distributed computing has significantly impacted modern data science practices by enabling the analysis of large datasets that were previously unmanageable on single machines. The ability to scale processing power by adding more nodes allows data scientists to work with real-time data and complex algorithms efficiently. Furthermore, distributed systems promote collaboration by allowing teams to share resources and workloads across different locations, facilitating the development of advanced analytical models and fostering innovation in various fields such as finance, healthcare, and social media analytics.

Related terms

MapReduce: A programming model used in distributed computing to process large data sets with a distributed algorithm on a cluster.

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

Parallel Processing: A method of computation in which many calculations or processes are carried out simultaneously to increase computational speed and efficiency.