Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique intersection known as graph thinking.
Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You'll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application.
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Dr. Denise Gosnell's passion for examining, applying, and evangelizing the applications of graph data was ignited during her apprenticeship under Dr. Teresa Haynes and Dr. Debra Knisley during her first NSF Fellowship. This group's work was one of the earliest applications of neural networks and graph theoretic structure in predictive computational biology. Since then, Dr. Gosnell has built, published, patented, and spoke on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals.
Currently, Dr. Gosnell is with DataStax where she aspires to build upon her experiences as a data scientist and graph architect. Prior to her role with DataStax, she built software solutions for and spoke at over a dozen conferences on permissioned blockchains, machine learning applications of graph analytics, and data science within the healthcare industry.
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