Abstract
We live in the data age and big data technologies allow to harness data, uncover hidden patterns and correlations in data, discover insights, and improve decision making. Although the Internet of Things (IoT) and big data have been evolved separately, they are closely intertwined. Indeed, the role of big data in IoT is tremendous as IoT is projected to include billions of connected devices, producing a massive amounts of data within a few years. In this context, big data analytics is a key enabler for unlocking the untapped potential of the IoT and fueling a wide range of data-driven products, services, and business processes. Apache Hadoop is the most prominent and used tool in big data. Hadoop is an open source framework that enables distributed processing of large data sets across commodity clusters. It is designed to scale with built-in high availability and reliability. Additional open source projects have been built around the original Hadoop implementation with the addition of Apache Spark. Spark is the next hype in the industry among the big data tools which was designed to address the shortcomings of Hadoop. Spark provides primitives for in-memory cluster computing which can speed jobs that run on the Hadoop data processing platform. This chapter discusses the details of top Big Data processing frameworks being used today enabling automatic and scalable insight discovery from large and complex data. In particular, we explain the origins of Hadoop and Apache Spark, their functionalities, architectures, and practical applications.
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Charles Babbage
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Balac, N. (2020). Big Data. In: Firouzi, F., Chakrabarty, K., Nassif, S. (eds) Intelligent Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-30367-9_6
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