While this article accurately captures the issues with traditional OLAP Cubes, it failed to recognize the latest development in this domain.
Projects like Apache Kylin, and its commercial version Kyligence, leverage modern computer architectures such as columnar storage, distributed processing, and AI optimization to build cubes over 100s of billions rows of data that covers 100s of dimensions. The performance result is unprecedented in either traditional OLAP cubes or today's MPP data warehouses. That's why the world's largest banks, retailers, insurance companies, and manufactures are turning to Kylin/Kyligence for the most challenging analytical problems.
Not to mention the rich semantic layer that modern OLAP cube technology provides, which greatly simplifies analytics architecture in the enterprises.
And, comparing columnar stores to OLAP cubes is like comparing apples to oranges. The former is a storage format and the latter is an analytical pattern. Modern OLAP cube technology like Kylin/Kyligence stores cubes in columnar stores anyway.