Large-scale single-cell RNA sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a dataset using a small subset of cells, which we refer to as a geometric sketch. Oursketches provide more comprehensive visualization of transcriptional diversity, capture rare cell typeswith high sensitivity, and reveal biological cell typesvia clustering. Our sketch of umbilical cord bloodcells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated. The construction of our sketches is extremely fast,which enabled us to accelerate other crucial resource-intensive tasks, such as scRNA-seq data integration, while maintaining accuracy. We anticipate our algorithm will become an increasingly essential step when sharing and analyzing the rapidly growing volume of scRNA-seq data and help enable the democratization of single-cell omics.