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"The dynamic development of digital technologies, especially those dedicated to devices generating large data streams, such as all kinds of measurement equipment temperature and humidity sensors, cameras, radio telescopes and satellites Internet of Things enables more in depth analysis of the surrounding reality, including better understanding of various natural phenomenon, starting from atomic level reactions, through macroscopic processes e.g. meteorology to observation of the Earth and the outer space. On the other hand such a large quantitative improvement requires a great number of processing and storage resources, resulting in the recent rapid development of Big Data technologies. Since 2015, the European Space Agency ESA has been providing a great amount of data gathered by exploratory equipment a collection of Sentinel satellites which perform Earth observation using various measurement techniques. For example Sentinel 2 provides a stream of digital photos, including images of the Baltic Sea and the whole territory of Poland. This data is used in an experimental installation of a Big Data processing system based on the open source software at the Academic Computer Center in Gdansk. The center has one of the most powerful supercomputers in Poland the Tryton computing cluster, consisting of 1600 nodes interconnected by a fast Infiniband network 56 Gbps and over 6 PB of storage. Some of these nodes are used as a computational cloud supervised by an OpenStack platform, where the Sentinel 2 data is processed. A subsystem of the automatic, perpetual data download to object storage based on Swift is deployed, the required software libraries for the image processing are configured and the Apache Spark cluster has been set up. The above system enables gathering and analysis of the recorded satellite images and the associated metadata, benefiting from the parallel computation mechanisms. This paper describes the above solution including its technical aspects."

"The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data.The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments."

"Rancangan awal real-time data pipeline sebelum optimasi dilakukan menggunakan sistem PubSub pada cloud yang menyimpan retensi data selama minimum 24 jam. Hal ini menyebabkan data yang telah terkumpul pada broker akan terus dikirimkan. Hal ini dapat mengakibatkan terjadinya pengulangan pengiriman data yang meningkatkan biaya overhead serta biaya operasional, waktu yang lebih lama untuk mentransmisikan data, dan duplikasi data yang menyebabkan tidak akuratnya data yang terkirim. Optimasi arsitektur yang diusulkan menggunakan database NoSQL dengan tujuan untuk memenuhi kebutuhan QoS level 2, latency, dan cost pada real-time data monitoring untuk healthcare. Hasil riset yang dicapai memenuhi kebutuhan monitoring data healthcare secara real-time dengan rancangan arsitektur yang diusulkan dan diimplementasikan pada cloud.

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The initial design of real-time data transmission architecture was carried out using the PubSub system in the cloud which stores data retention for a minimum of 24 hours so that data that has been collected at the broker will continue to be sent. This can result in repeated data transmissions that increase overhead and operational costs, longer time to transmit data, and data duplication which causes inaccurate data sent. The proposed architecture optimization uses a NoSQL database designed to meet QoS level 2, latency, and cost in real-time data monitoring for healthcare. The research results were achieved to meet the needs of monitoring healthcare data in real-time with the proposed architecture design and implemented in the cloud. "

"This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity.Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where scalable means that the computational and implementation complexities do not grow rapidly with the network size."

"Cloud networking : understanding cloud-based data center networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures, fabric technology, interconnections, and more. By the end of the book, readers will understand core networking technologies and how they’re used in a cloud data center."