February 2026: Delivered performance and maintainability improvements for Spark's vector aggregation suite. Key achievements include optimizing memory and compute paths for vector_avg and vector_sum, simplifying buffer management, and unifying the aggregate state with a common base trait. These changes reduce GC pressure, lower per-element overhead, and streamline future vector-aggregate development without altering user-facing behavior.

Month: 2026-01 | Focused on vector math enhancements in Spark SQL to enable embedding workflows and ML preprocessing inside the data platform. Delivered three feature clusters: (1) vector distance/similarity functions, (2) vector norm and normalization functions, and (3) vector-wide aggregations (vector_sum, vector_avg). Implemented robust type-safety, dimension validation, NULL handling, and optimized code generation paths (unrolled loops) to prepare for SIMD acceleration. Added extensive test coverage including SQL Golden tests for correctness and edge-cases, expression-schema updates, and unit tests for vector aggregations. Resulting capabilities enable in-SQL similarity search, clustering, and feature preprocessing on large datasets with reduced data movement and integration overhead. Technologies/skills demonstrated include Spark SQL internal expressions, ARRAY<FLOAT> handling, type-safety enforcement, error semantics, code generation optimizations, and test-driven development.