Transactional databases, commonly known as OLTP systems like PostgreSQL or MySQL, are versatile and capable of running various types of queries, making them suitable for structured, semi-structured, and unstructured data. However, for performance and scalability in handling large datasets, OLAP databases such as Oxla excel, especially when running queries over extensive data with mechanisms like partitions to enhance efficiency. In a benchmark comparison using the Star Schema Benchmark dataset, Oxla demonstrated faster query performance compared to PostgreSQL, highlighting OLAP's strengths in scenarios involving large-scale data analysis. OLAP databases use columnar storage, which enables quick column scanning and reduced storage size, making them easier to scale than OLTP systems, which focus on transactions and have limitations in scalability and indexing. Despite OLAP's advantages in cost-effective storage and scaling, they are not ideal for frequent data modifications due to constraints in updating single rows and the use of distributed object storage, which complicates row-level changes. Consequently, OLAP systems are best suited for large, relatively static datasets where modification needs are minimal, whereas OLTP databases are preferable for applications requiring frequent updates and transactions.