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SedonaDB: A new geospatial DataFrame library written in Rust
Apache Sedona is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark, Apache Flink, and Snowflake, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines.
SedonaDB extends the Sedona ecosystem with a single-node engine optimized for small-to-medium data analytics, delivering the simplicity and speed that distributed systems often cannot. Suppose you're analyzing ride-sharing data and want to identify which buildings are most commonly near pickup points, helping understand the relationship between trip origins and nearby landmarks, businesses, or residential structures that might influence ride demand patterns. For smaller datasets, however, Spark's distributed architecture can introduce unnecessary overhead, making it slower to run locally, harder to install, and more difficult to tune.
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