Database performance hinges on trade-offs, with indexes playing a crucial role in online-transactional systems by improving read performance at the cost of write efficiency. Different types of indexes cater to various data and queries, from scalar to geospatial and natural language search. In contrast, analytics databases often employ columnar storage, which optimizes queries for aggregating or summarizing data but can be less efficient for reading entire records or writing single entries. The challenge lies in optimizing storage formats for specific queries, as there isn't a one-size-fits-all solution due to the diversity of data types and performance metrics like query latency, write performance, and CPU utilization. GlareDB addresses this by supporting various storage engines and formats, including a native engine based on the deltalake protocol using Parquet, which offers compactness, columnar access properties, and per-column compression. Its flexible COPY TO operation allows for the materialization of queries to optimize workflows, illustrating how GlareDB accommodates diverse data systems and storage needs.