Big Data: Principles and best practices of scalable realtime data systems

By Nathan Marz, J. Warren. 2015

1,127 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a4b2fcd91638be1f98b0b49036ddf4b9d4aa196c

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Citations in the corpus

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “For example, the Lambda architecture [116] solves the problem of computing arbitrary functions on arbitrary data in real-time by decomposing the problem into three layers: the batch layer, the serving layer, and the speed layer (Fig.”
Mining big data: current status, and forecast to the future (2013, background): “An interesting proposal is the Lambda architecture of Nathan Marz [25].”
Big data and human geography (2013, background): “A survey of the emerging literature, however, denotes a number of key features (Boyd and Crawford, 2012; Dodge and Kitchin, 2005; Laney, 2001; Marz and Warren, 2012; Mayer-Schonberger and Cukier, 2013; Zikopoulos et al., 2012) – big data are:”
Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work (2014, methodology): “(boyd and Crawford 2012; Dodge and Kitchin 2005; Laney 2001; Marz and Warren 2012; Mayer-Schonberger and Cukier 2013; Zikopoulos et al., 2012) While there are varying estimates, depending on the methodology used, as to the growth of data production caused by big data (e.g., Hilbert and López 2009;…”
Domain-specific Knowledge Graphs: A survey (2020, background): “F) Computing performance in Big KGs: There is a notable consensus amongst the research society that the conventional technologies used to process and analysis the continuous propagation large-scale datasets are no longer adequate [205].”
The real-time city? Big data and smart urbanism (2013, background): “(see Boyd and Crawford 2012; Dodge and Kitchin 2005; Laney 2001; Marz and Warren 2012; Mayer- Schonberger and Cukier 2013; Zikopoulos et al. 2012).”
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, background): “The Lambda Architecture (LA) [91] is a paradigm 1135”
Online Machine Learning in Big Data Streams (2018, background): “In terms of simplicity, the most important goal is to overcome the need to integrate the DSPE with a batch processing engine as in the so-called lambda architecture described, among other places, in [167, 133].”
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets (2016, background): “extensionality (can add/change new fields easily) and scaleability (can expand in size rapidly) (Marz and Warren, 2012); .”
Bringing up OpenSky: A large-scale ADS-B sensor network for research (2014, methodology): “Since the collected sensor data is intended for research, the database is built after the immutability paradigm: nothing gets updated or changed, new data is simply attached [7].”
Internet of Things: An Overview (2017, unknown): “The IoT-A[16] focuses on the development and validation of an integrated IoT network architecture and supporting building blocks, with the objective to be “the European Lighthouse Integrated Project addressing the Internet-of-Things Architecture”.”