Cloud Native Geospatial Analytics with Apache Sedona: A Hands-On Guide for Working with Large-Scale Spatial Data

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.

Jia Yu is a co-founder of Wherobots, a venture-backed company for helping businesses to drive insights from spatiotemporal data. He was a Tenure-Track Assistant Professor of Computer Science at Washington State University from 2020 to 2023. He obtained his Ph.D. in Computer Science from Arizona State University. His research focuses on large-scale database systems and geospatial data management. In particular, he worked on distributed geospatial data management systems, database indexing, and geospatial data visualization. Jiaâ s research outcomes have appeared in the most prestigious database / GIS conferences and journals, including SIGMOD, VLDB, ICDE, SIGSPATIAL and VLDB Journal. He is the main contributor of several open-sourced research projects such as Apache Sedona, a cluster computing framework for processing big spatial data, which receives 1 million downloads per month and has users / contributors from major companies.

Mo Sarwat is the CEO of Wherobots, co-creator of Apache Sedona, and an associate professor at Arizona State University. At Wherobots he is spearheading a team developing a cloud data platform equipped with a brain and memory for our planet to solve the world's most pressing issues. Wherobots is founded by the creators of Apache Sedona, an open-source framework designed for large-scale spatial data processing in cloud and on-prem deployments. At Arizona State University Mo teaches and conducts research in the fields of large-scale data processing, databases, data analytics, and AI data infrastructure. With over a decade of experience in academia and industry, Mo has published more than 60 peer-reviewed papers, received two best research paper awards, been named an Early Career Distinguished Lecturer by the IEEE Mobile Data Management community, and is also a recipient of the 2019 National Science Foundation CAREER award, one of the most prestigious honors for young faculty members. His mission is to advance the state of the art in data management and AI, to empower data-driven decision making for a wide range of applications, such as transportation, mobility, and environmental monitoring. He is passionate about developing robust and scalable data systems that can handle complex and massive datasets, and leverage artificial intelligence and machine learning techniques to extract valuable insights and patterns.