Cost is a big factor the author underestimated in this big data era. Precalculated cube is not only faster but also times cheaper in the cloud, thanks to the reuse of precalculated result.
Dynamic query services in the cloud basically charge by processed data volume, like Google BigQuery and Amazon Redshift/Athena. For small and medium dataset, this works well. But for big data close to or above billions of rows, the cost will make you reconsider.
In the recent Apache Kylin Meetup in Berlin, OLX Group shared their comparison between OLAP cube and dynamic query in real case. Given 0.1 billion rows, cube technology (Apache Kylin and SSAS) prevails over MPP+Columnar (Redshift) easily. Especially Apache Kylin is 3.8x faster and 4.4x cheaper than Redshift for their business.
(https://www.slideshare.net/TylerWishnoff/apache-kylin-meetup...)
For me, a mix of precalculation (80%) and dynamic calculation (20%) should hit the sweet point between cost effectiveness and query flexibility.
Dynamic query services in the cloud basically charge by processed data volume, like Google BigQuery and Amazon Redshift/Athena. For small and medium dataset, this works well. But for big data close to or above billions of rows, the cost will make you reconsider.
In the recent Apache Kylin Meetup in Berlin, OLX Group shared their comparison between OLAP cube and dynamic query in real case. Given 0.1 billion rows, cube technology (Apache Kylin and SSAS) prevails over MPP+Columnar (Redshift) easily. Especially Apache Kylin is 3.8x faster and 4.4x cheaper than Redshift for their business. (https://www.slideshare.net/TylerWishnoff/apache-kylin-meetup...)
For me, a mix of precalculation (80%) and dynamic calculation (20%) should hit the sweet point between cost effectiveness and query flexibility.