Have you ever wondered why most existing database systems focus solely on either analytical or transactional performance? The data orientation within the file format and the internals of the storage engine are key reasons for this specialization. Current database systems are unable to balance transactional and analytical processing, and therefore, are forced to optimize for just one of both workload types. Transaction-focused OLTP systems use row-based storage formats for quick updates and lookups, but these formats are inefficient for analytics-focused OLAP tasks. OLAP workloads require scanning many rows while typically accessing only a few columns, and row-based formats are simply not designed to handle this. OLAP systems use compressed columnar formats for fast scans, which make updates complex and slow, often lacking efficient point lookup capabilities. For instance, a simple table scan using a column store can be more than 5x faster than storing data in a row-based format due to the data movement characteristics of the format.