👉 DF computing, or Distributed Frameworks Computing, is a paradigm that enables the execution of large-scale data processing tasks across multiple computing nodes in a distributed environment. It leverages frameworks like Apache Spark, Flink, or Dask to manage and orchestrate computations across clusters of machines, allowing for parallel processing of data. This approach is particularly useful for handling big data, as it distributes the workload and optimizes resource utilization, significantly reducing processing time and enhancing scalability. By breaking down complex tasks into smaller, manageable units that can be executed concurrently, DF computing facilitates efficient and effective data analysis, making it a cornerstone of modern data science and machine learning workflows.
