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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 Paweł 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, build geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.
- Understand how Apache Sedona helps data practitioners address challenges with geospatial data
- Learn how to run Apache Sedona, both locally and in cloud environments
- Efficiently load, query, and analyze geospatial datasets using spatial SQL
- Employ machine learning techniques to derive strategy-defining insights from spatial data
- Manage and optimize large-scale geospatial data within a data lakehouse architecture
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 Paweł 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, build geospatial data lakehouses, and handle modern geospatial data formats like GeoParquet.
- Understand how Apache Sedona helps data practitioners address challenges with geospatial data
- Learn how to run Apache Sedona, both locally and in cloud environments
- Efficiently load, query, and analyze geospatial datasets using spatial SQL
- Employ machine learning techniques to derive strategy-defining insights from spatial data
- Manage and optimize large-scale geospatial data within a data lakehouse architecture
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.
| Erscheinungsjahr: | 2025 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781098173999 |
| ISBN-10: | 1098173996 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Tokaj, Pawel
Yu, Jia Sarwat, Mo |
| Hersteller: | O'Reilly Media |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 232 x 175 x 21 mm |
| Von/Mit: | Pawel Tokaj (u. a.) |
| Erscheinungsdatum: | 31.12.2025 |
| Gewicht: | 0,546 kg |
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