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Discrete Global Grid Systems (DGGS) and in particular HEALPix have become increasingly popular in the Earth sciences over the past few years, mainly due to its equal-area nature and readily available python libraries. However, this adoption and the ever increasing amounts of data come with its own set of challenges. In particular, the existing python libraries are written for use in astronomy and thus only work on a sphere, resulting in small but often non-negligible variations in the cell areas when applied to the surface of the earth.Additionally, a large spatial coverage at high resolutions require a large amount of memory just to represent the spatial information in memory (e.g. roughly 100GB for full Earth coverage at 100 m resolution).Finally, the storage format of HEALPix was not standardized until very recently with the release of the CF conventions version 1.13 and the upcoming zarr DGGS convention.We present healpix-geo, a HEALPix implementation library for python built on top of the cdshealpix, moc, and geodesy rust crates with minimal python dependencies (numpy and optionally shapely). It supports the most common HEALPix indexing schemes (nested, ring, zuniq), allows the conversion of cell indices to and from ellipsoidal coordinates, and contains a range-based data structure suitable to index a large amount of cells with a small memory footprint.We further show how healpix-geo integrates with xdggs, an xarray extension that enables high-level interaction with DGGS datasets, including efficient subsetting, analysis-ready representations, and visualization within Pangeo workflows. xdggs also provides an extensible mechanism to easily import/export DGGS data from/to a variety of models or conventions, with built-in support of the CF HEALPix conventions and the zarr DGGS conventions. Together, healpix-geo and xdggs provide an end-to-end, standards-aligned pathway for scalable HEALPix-based geospatial analysis on the ellipsoid.