SAP HANA's column-based storage significantly accelerates data reading through two primary mechanisms. Firstly, it enables massive parallel processing. Since all values for a single column are stored contiguously in memory, this block of data can be easily partitioned and processed simultaneously by multiple CPU cores. Secondly, columnar storage allows for very high data compression ratios. Data within a column is homogenous (same data type) and often has low cardinality, which is ideal for compression algorithms. Reading smaller, compressed data from memory into the CPU cache is much faster as it reduces the data transfer volume.

C. A column is read-optimized by reading from the delta store only.

This is incorrect. Read operations must combine data from the highly compressed, read-optimized main store and the write-optimized delta store to provide a complete, current view of the data.

D. A column contains all of the information for one record.

This statement describes row-based storage, not column-based. In a columnar store, a column contains a single attribute's value for all records, not all attributes for a single record.

1. SAP Help Portal

SAP HANA Platform 2.0 SPS 07

SAP HANA Administration Guide for SAP HANA Platform

Section: "Columnar Data Storage".

This section explicitly states: "High data compression rates are possible because the data in a column is of the same type... Columnar data storage also makes it possible to execute operations in parallel by processing different columns or different sections of a column at the same time." This directly supports answers A and B.

2. SAP Help Portal

SAP HANA Platform 2.0 SPS 07

SAP HANA Administration Guide for SAP HANA Platform

Section: "Main Storage and Delta Storage".

This section details the delta merge process

explaining that for read access

"data has to be read from both the main storage and the delta storage and the results have to be merged." This confirms that option C is incorrect.

3. Plattner

H. (2014). A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD '13). Association for Computing Machinery

New York

NY

USA

1–2.

This academic paper by a co-founder of SAP explains the foundational principles of SAP HANA. It highlights that columnar organization is key to both high compression rates and parallel execution of queries on modern multi-core CPUs

directly supporting options A and B. It also contrasts this with row-based stores

invalidating option D.