provide response to this in one paragraph "OLAP cubes are everywhere within the company work environment because of their great features on analyzing large multidimensional datasets.

Fast queries: OLAP cubes are designed to provide fast queries across large sets of data and can keep response times low even on billions of rows that is why; analytics still remains responsive. The data is in pre$-$aggregated form and the amounts are saved using an analytical query that allows multidimensional structuring.

Intuitive analysis: Users can, therefore, use OLAP cubes and view the data in pivot tables, charts among other visualizations by slicing and dicing information across various dimensions. This enables clear visibility of insights and trends within the data.

Flexible granularity: It is possible for the users to drill down so far as they want as well as roll up out of convenience and effective analysis.

Wide accessibility: Many BI platforms provide access to OLAP query and reporting tools that can be used by both technical users as well as those with no particular ICT knowledge through user$-$friendly drag$-$and drop interfaces.

I have no practical experience in working with implementation of OLAP cubs. However, everyday applications used in non$-$retail businesses involve sales metrics performance analysis; financial KPIs; operational analytics and so on$.$ For instance, an online retailer might apply OLAP cube for planning and trafficking dynamics of web sales volume per products region channel date at high or low levels.

When it comes to data architecture, the OLAP cubes would lie inside of analytics $/$ BI layer. They generate their data from defined sources such as transactional platforms, warehouses or lakes providing all users the opportunity to simply analysis that consolidated and standardized information. Thus, OLAP assists in analytics based on core data management platforms. It is the ability to analyze that unified data and then apply it to real situations in business that makes cubes so helpful for analysis.

So$,$ OLAP cubes are included in the Business Intelligence$/$Analytics layer among other components of a typical data architecture.

Raw data source $($operational system, external data$)$

ETL, data warehouses and data lakes for integration as well as storage of information.

Security $($data governance in terms of policy and metadata management$).$

Business intelligence and analytics

Consumption$/$presentation $($reporting$,$ dashboards, applications$)$

In this architecture, the OLAP cubes would be found under Business Intelligence and Analytics layer.

OLAP cubes:

Clean, consolidated data from sources such as data warehouses and lakes

Allow for advanced analytical processing on this consolidated information.

Enhance functionality such as drilling, pivoting and slicing and dicing.

Visualization of power data tools and dashboards in BI

Therefore, the primary benefit of OLAP cubes is providing flexible and fast analytics over consolidated reliable sources. The layers under data architecture are responsible for acquisition, caching of the information; cleaning and integration among them. The upper layers containing the OLAP, and visualization are responsible for complex analytical processing, insights and presentation.

Thus, I would place OLAP cubes as an essential part within the Business Intelligence layer of a strong current data architecture that consumes well$-$structured information and supply power to advanced analytics applications.

Smith, J$.$ A; Johnson et al$,.(2024).$ Enhancing Business Intelligence: A broad overview of OLAP cubes and data analytics.