How does HeatWave address the challenges of Online Analytical Processing (OLAP) compared to traditional row-based...

HeatWave's Columnar Processing:

HeatWave improves OLAP performance by storing data in acolumnar formatin memory. This format is particularly efficient foranalytical queriesbecause:

It minimizes the amount of data read by only scanning thenecessary columns.

Columnar storage supportsvectorized processing, which significantly speeds up aggregation operations.

Beingin-memory, it eliminates disk I/O bottlenecks, crucial for high-speed analytics.

Why the other options are incorrect:

A:Indexing is useful, but columnar storage is the primary driver of speed.

C:Caching improves access times but does not address the fundamental OLAP challenge of processing large datasets.

D:HeatWave distributes workload usingparallel processing, not simple sharding.

E:AI-powered indexing is not a core feature of HeatWave.