The Power Of Resilient Distributed Datasets

Ramandeep Kaur explains just how powerful Resilient Distributed Datasets are:

A fault-tolerant collection of elements that can be operated on in parallel:  “Resilient Distributed Dataset” a.k.a. RDD

RDD (Resilient Distributed Dataset) is the fundamental data structure of Apache Spark which are an immutable collection of objects which computes on the different node of the cluster. Each and every dataset in Spark RDD is logically partitioned across many servers so that they can be computed on different nodes of the cluster.

RDDs provide a restricted form of shared memory, based on coarse-grained transformations rather than fine-grained updates to shared state.

Coarse-grained transformations are those that are applied over an entire dataset. On the other hand, a fine grained transaction is one applied on smaller set, may be a single row. But with fine grained transactions you have to save the updates which can be costlier but it is flexible than a coarse grained one.

Read on for more about the fundamental data structure in Spark.

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