Apache Spark is an open-source cluster computing framework which provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Apache Spark offers a unique application programming interface centred on a data structure called resilient distributed dataset. This allows implementation of iterative algorithms. It requires cluster management and distributed storage systems. For Cluster Management it supports Hadoop YARN and/or Apache Mesos. For Distributed Storage Systems it supports MapR File Systems, Cassandra, Hadoop Distributed File Systems, etc.
Spark Core is the foundation, it offers distributed task dispatching, scheduling and basic I/O functionalities exposed through application programming interface, e.g., Java, Python, Scala, centred on the resilient distributed dataset. Spark also provides two restricted forms of shared variables broadcast variables and accumulators.
Spark SQL is component present on top of the Spark Core that brings up new data abstraction called Data Frames. Data Frames supports structured and semi-structured data
Spark Streaming performs streaming analytics by leveraging Spark’s fast scheduling capability. It enters data in mini-batches and performs RDD transformation on those mini-batches of data.
MLib Machine Learning Library:
Spark’s MLlib is a distributed machine learning framework on top Spark Core. It is nine times as fast as disk-based implementation used by Apache Mahout and scales better than Vowpal Wabbit.
GraphX is a distributed graph processing framework present on Apache Spark. It offers API for graph computation and optimized runtime for abstraction
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1. Basic computer handling skills.
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