Modern enterprise applications and data warehouse systems move data into data lakes for economical and scalability reasons. Data is then stored in popular columnar file formats like Parquet which are optimized for writing using open table formats like Iceberg or Delta. This presents new challenges for existing database systems and their execution engines because excellent performance and scalability when accessing this data in complex analytical queries is expected while data is located in a remote data lake. In this work, we present how we adapted the HANA Cloud Database Engine for efficient processing of files in data lakes, which we call SQL-on-Files (SoF). We motivate this evolution by its relevance for Business Data Cloud, SAP's Lakehouse, we discuss the viability of general architecture choices like pushdown and direct access architectures, and give insights into our SoF design decisions towards scalable, analytical query processing around execution engine, optimizer and caching. Our evaluation of SoF shows benefits of direct access over pushdown architectures for a new warehouse benchmark with complex, analytical workloads.

A soft error redirection is a URL redirection to a page that returns the HTTP status code 200 (OK) but has actually no relevant content to the client request. Since such redirections degrade the performance of web search engines in many ways, it is highly desirable to remove as many of them as possible. We propose a novel approach to detect soft error redirections by analyzing redirection logs collected during crawling operation. Experimental results on huge crawl data show that our measure can classify soft error redirections effectively.