Scalable IOT with Apache Cassandra.
IOT and Event Based systems can process huge volumes of data. Which typically needs to be stored and read in near real time for event processing, in addition to being read in bulk to feed data hungry learning systems. Apache Cassandra provides a high performance, scalable, and fault tolerant database platform with excellent support for time series data models typically seen in IOT systems. It's millisecond (or better) latency can support systems that react to events in real time, while scalable bulk reads via batch processing systems such as Apache Hadoop and Apache Spark can support learning applications. These features, and more, make Cassandra an ideal persistence platform for modern data intensive, event driven, systems.
In this talk Aaron Morton, CEO at The Last Pickle, will discuss lessons learned using Cassandra for IOT systems. He will explain how Cassandra fits into the modern technology landscape and dive into data modelling for common IOT use cases, capacity planning for huge data loads, tuning for high performance, and integration with other data driven systems. Whether starting a new project, or deep into the weeds on an existing system, attendees will leave will leave with an understanding of how Apache Cassandra can help build robust infrastructure for IOT systems.
Anyone interested in scalable IoT with Apache Cassandra
schedule Submitted 3 years ago
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Daniel Filonik - A Geometric Approach towards Data Analysis and VisualisationDaniel FilonikPostdoctoral FellowEPICentre, UNSW Art and Design
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Beginning with the work of Bertin, visualisation scholars have attempted to systematically study and deconstruct visualisations in order to gain insights about their fundamental structure. More recently, the idea of deconstructing visualizations into fine-grained, modular units of composition also lies at the heart of graphics grammars. These theories provide the foundation for visualization frameworks and interfaces developed as part of ongoing research, as well as state-of-the-art commercial software, such as Tableau. In a similar vein, scholars like Tufte have long advocated to forego embellishments and decorations in favor of abstract and minimalist representations. They argue that such representations facilitate data analysis by communicating only essential information and minimizing distraction.
This presentation continues along such lines of thought, proposing that this pursuit naturally leads to a geometric approach towards data analysis and visualisation. Looking at data from a sufficiently high level of abstraction, one inevitably returns to fundamental mathematical concepts. As one of the oldest branches of mathematics, geometry offers a vast amount of knowledge that can be applied to the formal study of visualisations.
``Visualization is a method of computing. It transforms the symbolic into the geometric.'' (McCormick et al., 1987)
In other words, geometry is the mathematical link between abstract information and graphic representation. In order to graphically represent information, we assign to it a geometric form. In this presentation we will explore the nature of these mappings from symbolic to geometric representations. This geometric approach provides an alternative perspective towards analysing data. This perspective is inherently equipped with high-level abstractions and invites generalization. It enables the study of abstract geometric objects independent from a concrete presentation medium. Consequently, it allows to interpret data directly through geometric primitives and transformations.
The presentation illustrates the geometric approach using diverse examples and illustrations. In turn, we discuss the opportunities and challenges that arise from this perspective. For instance, a key benefit of this approach is that it allows to consider seemingly disparate visualization types in a unified framework. By systematically enumerating the design space of geometric representations, it is possible to trivially apply extensions and modifications, resulting in great expressiveness. The approach naturally extends to visualisation techniques for complex, multidimensional, multivariate data sets. However, the effectiveness of the resulting representations and cognitive challenges in the interpretation require careful consideration.