11/20/2023 0 Comments Olap cube vs tabular modelAs Data Lakes makes it easier to access data for data-savvy persons, you should always keep in mind where you put which data. What you also see is the difference between corporate data that traditionally go into the Data Warehouse (DWH) and the real-time streaming data like social media, IoT that goes into the data lake. To illustrate this I like the architecture from DWBI1, which shows the architecture as generic as possible: In my opinion, Data Lake is the buzzword for it where everyone easily can access data instead of BI-Specialists only or worse, infrastructure guys having access to the FTP-Server. One reason is that nowadays are countless persons wish to make use of data, that’s why you might want to make it available to all of them or at least have a more open architecture. But before I go into more dept what I found, I want that you look at the architecture as a whole. Therefore I was researching for alternatives on the world wide web. The query language MDX is considerably hard to understand and difficult to write more complex queries. It uses Microsoft Visual Studio to add or edit the cubes with is hard to source-control and make it impossible to modify for any other data-savvy person apart from the BI-Specialist.It is not distributed and cannot be parallelised as any new modern technologies (containers, clusters, etc.).Does not fit in the open-source and big data ecosystem due to.It was developed a long time ago it is not optimised for the vast amount of data nowadays. There is a limit to the size of data it can process fast.The multi-dimensional model of Analysis Services are not supported in the Azure Cloud and also does not look like it will soon (there is the tabular model of Microsoft cubes, but they still got some following limitations).There are several problems I see or encountered: Nevertheless, these cubes are getting more and more to their limits as you might have experienced that in your own company. These are a compelling and fast way to aggregate and pre-calculate all of your corporate-related data and makes it available to your operational business users. As the Microsoft BI stack is still used widely around in small to large companies, the technology for it is mostly SQL Server Analysis Services (SSAS) or Microsoft Cubes. Why replace OLAP-Cubes?Īs my job as a business intelligence specialist and data engineer, I was always using OLAP-Technologies for quick ad-hoc analysis with tools like Tableau, TARGIT, Power BI, Excel, etc. Many other vendors of multidimensional databases have adopted MDX for querying data, but with this specific language, management of the cube requires personnel with the skill set. In order to manage and perform processes with an OLAP cube, Microsoft developed a query language, known as multidimensional expressions ( MDX), in the late 1990s. In OLAP cubes, data (measures) are categorised by dimensions. An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes. An OLAP cube is a multidimensional database that is optimised for data warehouse and online analytical processing (OLAP) applications. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modelling. OLAP is an acronym for Online Analytical Processing. Are you on the lookout for a replacement for the Microsoft Analysis Cubes, are you looking for a big data OLAP system that scales ad libitum, do you want to have your analytics updated even real-time? In this blog, I want to show you possible solutions that are ready for the future and fits into existing data architecture.
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