Navigating the complexities of large-scale spatial data can be daunting. In order to unleash the power of massive and complex datasets, you'll need a cutting-edge tool like Apache Sedona. This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture, climate science, and more. This book serves as your guide to leveraging this tool, along with other technologies, to unlock the potential of geospatial analytics.
Authors Pawel Tokaj, Jia Yu, and Mo Sarwat provide practical solutions to the challenges of working with geospatial data at scale. Ideal for developers, data scientists, engineers, and analysts, this guide uses real-world examples to help you integrate Python data ecosystems, apply machine learning, construct geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.
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Pawel Tokaj is a staff software engineer at Splunk and a PMC member of the Apache Sedona project who enjoys writing reliable, efficient software that helps others. His love for geospatial data started at the Warsaw University of Technology, where he graduated in geodesy and cartography.
Pawel's primary focus areas are distributed databases and systems, cloud computing, and geospatial data processing. He believes that open source projects make knowledge more accessible; he has contributed to Apache Sedona, Open Lineage, and Airbyte. He attends various conferences or meetups where he shares his knowledge as a speaker or participant. He is a technology nerd, spending a lot of his spare time reading books and articles and developing open source software.
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Paperback. Zustand: new. Paperback. Navigating the complexities of large-scale spatial data can be daunting. In order to unleash the power of massive and complex datasets, you'll need a cutting-edge tool like Apache Sedona. This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture, climate science, and more. This book serves as your guide to leveraging this tool, along with other technologies, to unlock the potential of geospatial analytics.Authors Pawel Tokaj, Jia Yu, and Mo Sarwat provide practical solutions to the challenges of working with geospatial data at scale. Ideal for developers, data scientists, engineers, and analysts, this guide uses real-world examples to help you integrate Python data ecosystems, apply machine learning, construct geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.Understand how Apache Sedona helps data practitioners address challenges with geospatial dataLearn how to run Apache Sedona, both locally and in cloud environmentsEfficiently load, query, and analyze geospatial datasets using spatial SQLEmploy machine learning techniques to derive strategy-defining insights from spatial dataManage and optimize large-scale geospatial data within a data lakehouse architecture This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture, climate science, and more. This book serves as your guide to leveraging this tool, along with other technologies, to unlock the potential of geospatial analytics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781098173999
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Paperback. Zustand: New. Navigating the complexities of large-scale spatial data can be daunting. In order to unleash the power of massive and complex datasets, you'll need a cutting-edge tool like Apache Sedona. This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture, climate science, and more. This book serves as your guide to leveraging this tool, along with other technologies, to unlock the potential of geospatial analytics.Authors Pawel Tokaj, Jia Yu, and Mo Sarwat provide practical solutions to the challenges of working with geospatial data at scale. Ideal for developers, data scientists, engineers, and analysts, this guide uses real-world examples to help you integrate Python data ecosystems, apply machine learning, construct geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.Understand how Apache Sedona helps data practitioners address challenges with geospatial dataLearn how to run Apache Sedona, both locally and in cloud environmentsEfficiently load, query, and analyze geospatial datasets using spatial SQLEmploy machine learning techniques to derive strategy-defining insights from spatial dataManage and optimize large-scale geospatial data within a data lakehouse architecture. Bestandsnummer des Verkäufers LU-9781098173999
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Paperback. Zustand: New. Navigating the complexities of large-scale spatial data can be daunting. In order to unleash the power of massive and complex datasets, you'll need a cutting-edge tool like Apache Sedona. This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture, climate science, and more. This book serves as your guide to leveraging this tool, along with other technologies, to unlock the potential of geospatial analytics.Authors Pawel Tokaj, Jia Yu, and Mo Sarwat provide practical solutions to the challenges of working with geospatial data at scale. Ideal for developers, data scientists, engineers, and analysts, this guide uses real-world examples to help you integrate Python data ecosystems, apply machine learning, construct geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.Understand how Apache Sedona helps data practitioners address challenges with geospatial dataLearn how to run Apache Sedona, both locally and in cloud environmentsEfficiently load, query, and analyze geospatial datasets using spatial SQLEmploy machine learning techniques to derive strategy-defining insights from spatial dataManage and optimize large-scale geospatial data within a data lakehouse architecture. Bestandsnummer des Verkäufers LU-9781098173999
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