Cloud Native Geospatial Analytics with Apache Sedona

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 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 an active committer and key developer on the Apache Sedona (incubating) project, where he authored the Sedona Python API to enhance data practitioners experience in handling spatial data. Pawel's contributions are recognized in his industry, with colleagues and mentors alike valuing his deep expertise in Apache Spark, Hadoop, and streaming solutions, alongside his talent for turning raw data into polished insights. Whether he's designing secure data pipelines for fintech startups or presenting his insights at tech conventions, Pawel combines his love for innovation with a genuine knack for building impactful solutions.