Cut multi-file query times ~80% by using spatial indexing

Executive Summary

Enterprises shifting analytics off warehouses and onto Parquet/Arrow data lakes increasingly pay a steep performance tax for multi-file scans: across an addressable population of roughly 200,000 enterprises, many mission-critical queries today touch thousands of small files, producing 3–10x higher latency and proportional increases in cloud compute and egress spend. This problem is particularly acute for telemetry, geospatial, and time-series workloads—IoT, telco, utilities, and observability teams routinely struggle with queries that return small spatial or temporal slices but still scan large swaths of object storage. A practical product is an external, spatial/time-aware indexing layer for Parquet/Arrow datasets that integrates with engines like Spark, Trino, and DuckDB and with query planners via cost-model hints. By using proven spatial structures (R-tree/Z-order) augmented with learned index techniques and automatic index selection, you can prune 70–90% of files for targeted queries and cut multi-file query times by roughly 80%, while keeping index storage overhead to well under 1–3% of the dataset. Key engineering work will be connectors, background index maintenance, and strong correctness guarantees for high-churn datasets, which are non-trivial but solvable problems. This market is unusually attractive now: a $60B estimated spend on cloud data platform and analytics, broad Parquet/Arrow standardization, explosive growth in edge telemetry, and emerging AI-driven optimizers that make automatic index usage feasible. Competition is medium and fragmented—some query engines are adding pruning features, but few vendors focus on spatial/time semantics with learned-index tuning and an open-format-first approach; differentiation will hinge on demonstrable petabyte-scale reliability, clear ROI case studies, and partnerships with major query engines.