Definition of Distributed Computing Frameworks
Distributed computing frameworks are software systems designed to facilitate the distributed processing of data or computational tasks across multiple interconnected computers or nodes in a cluster or grid. These frameworks provide a set of tools, libraries, and programming models that enable developers and data engineers to build and execute applications that can leverage the power of multiple machines working together.
What is the role of distributed computing frameworks in data engineering?
Distributed computing frameworks play a crucial role in data engineering by enabling the processing and analysis of large-scale data sets across multiple machines or nodes in a cluster. They provide a scalable and efficient way to handle big data workloads that cannot be effectively processed by a single machine.
Distributed computing frameworks enable scalable, efficient processing of large datasets across many machines, offering fault tolerance, flexible data handling, and cost-effectiveness in data engineering tasks.
Table of Content
- Distributed Computing Concepts for Data Engineers
- Key roles and benefits of distributed computing frameworks in data engineering
- Examples of Distributed Computing Frameworks
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