Business intelligence systems often rely on wide tables—pre-joined datasets that avoid complex SQL joins. While these tables are fast for queries, they introduce data redundancy, violate normal forms, and lack flexibility. The core problem is that relational database joins are notoriously slow, making pre-joined tables a pragmatic, if flawed, performance shortcut.

However, the performance gains from wide tables come at a cost: massive I/O overhead from reading duplicated data. A simple query can pull gigabytes of redundant information, slowing analytics. This trade-off reveals a fundamental limitation: SQL cannot optimize joins for specific BI scenarios, leaving developers stuck between slow queries and bloated data models.

A new open-source engine, SPL (Structured Process Language), aims to solve this by handling joins with specialized algorithms for foreign and primary keys. Tests against ClickHouse and StarRocks show SPL's real-time joins are 3-9x faster than standard SQL joins and surpass wide-table performance entirely, eliminating the need for redundant pre-joined data.