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What is Resilient Distributed Datasets (RDDs)

Chapter 12
Handbook of Research on Big Data Storage and Visualization Techniques
Resilient Distributed Datasets (RDDs) are a distributed memory abstraction that lets programmers perform In-Memory Computations on large clusters in a Fault Tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: Iterative Algorithms and Interactive Data Mining Tools. Formally, an RDD is a read-only, partitioned collection of records. RDDs can only be created through deterministic operations on either (1) data in Stable Storage or (2) other RDDs. (Zaharia, 2012 AU68: The in-text citation "Zaharia, 2012" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. )
Published in Chapter:
Distributed Streaming Big Data Analytics for Internet of Things (IoT)
Sornalakshmi Krishnan (SRM University, India) and Kayalvizhi Jayavel (SRM University, India)
Copyright: © 2018 |Pages: 36
DOI: 10.4018/978-1-5225-3142-5.ch012
Abstract
In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.
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