LocationTech RasterFrames is a Scala library (with planned Python bindings) built on top of Apache Spark SQL and GeoTrellis. It also has dependencies on LocationTech's SFCurve, as well as a subset of GeoMesa. Its focus is to read and write against multiple imagery formats and stores, through the Spark SQL DataSource API, and make their contents available as columns of tiles with associated spatial metadata (such as indexes, keys, extents, projections, reference systems, etc). Conceptually, the contents of a RasterFrame is akin to the layers in a GIS application (e.g. QGIS), where each layer (or column in the data frame) represents a spectral band or other georectified, rasterized data product. Multiple RasterFrames may be spatially joined with each other, and their rows filtered, columns combined, statistically summarized, or have any number of additional map algebra-like operations applied to them. Furthermore, RasterFrames also includes interoperability with SparkML, allowing for classical machine learning algorithms to be applied to features derived from RasterFrame columns.