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Multidimensional vs Tabular SSAS models: we support both

One common motivation to use Microsoft SQL Server Analysis Services is the analysis of massive datasets. OLAP cubes allow coping with much more significant data volumes than relational databases. However, many users claimed that multidimensional cubes were hard to understand, especially when designing the model.

That was why Microsoft introduced the SQL Server Analysis Services (SSAS) tabular model in 2012. It has become widely popular since then. The tabular model is something in between relational databases and multidimensional cubes. Similar to databases, the tabular model supports tables with relations. Similar to cubes, the model supports measures and key performance indicators (KPIs).

So, here comes the question:

Which model to choose?

As far as choosing the model goes, it’s better to conduct a detailed study based on project requirements. But we’ll guide you with core points to consider before giving preference to a specific solution. 

Why choose a tabular solution:

  • easier for understanding and creating the model;
  • works quicker than multidimensional cubes for queries based on columns;
  • hardware, such as disks, is not essential. However, tabular is a memory dependent solution, and more memory will ensure better performance;
  • more efficient data compression about one-tenth of the size, whereas compressed multidimensional data takes up a third of the size of the original database

Why choose a multidimensional solution

  • works better with a large amount of data – when we are talking about terabytes, it’s better to go with the multidimensional database. If your database requires more than five terabytes, multidimensional is the only option.
  • performs better in terms of scalability
  • some features, such as aggregations or actions, are supported in the multidimensional model only

OLAP analysis and visualization

Say you’ve chosen the model that works best for your project. Now it’s essential to find a way to extract meaningful information from OLAP data. This is where data visualization tools come to the rescue. Such tools help you spot trends in your tabular or multidimensional data and communicate them at maximal efficiency.

One way to bring your OLAP data to life is via reports with pivot tables and pivot charts.

As a form of data visualization, tables are beneficial for comparative data analysis. Pivot charts complement and enhance this visualization type, making information easier to grasp. Combined, they can make up powerful data dashboards or be a part of OLAP business intelligence.

How to choose the best OLAP tools

When choosing a suitable data visualization tool, we recommend paying attention to these criteria:

  • seamless connection to OLAP cubes
  • the flexibility of integration with modern web and desktop technologies
  • support of slicing, pivoting, rolling up, and drilling down – essential operations for data cube analysis
  • a comfy and accessible user interface

End-users should also be able to explore their data interactively without generating complex queries.

Another critical aspect is performance. OLAP reporting should be not only insightful but also swift to respond to emerging data analysis goals. 

Having all this in mind, we designed Flexmonster Pivot to be a front-end component for OLAP visualization.

And knowing how large your data cubes can be, we optimized the tool to handle any size datasets, making visualization and reporting fast and reliable

Which model does Flexmonster support?

Our development team believes that both tabular and multidimensional solutions can successfully meet diverse business and user requirements. That’s why Flexmonster Pivot Table & Charts supports both models

Your end-users can work with data from tabular or multidimensional data cubes – OLAP data analysis will be rich and interactive in each case. 

How to build OLAP-based pivot table reports

To find patterns in your data, just connect to the cube from the pivot table and use the Field List as a pivot table designer: drag and drop hierarchies to rows, columns, and measures.

Now your report layout is ready. Yes, it’s that simple!

You can also look at your OLAP data from another angle by visualizing it with pivot charts that are drillable and interactive.

Here is how to switch to the pivot charts mode in one click:

And here you go! You can now share your resultsexport a report to PDF, Excel, HTML, or whatever format suits you best.


Get started with JavaScript pivot table for OLAP

Want to get your OLAP data visualization tool up and running quickly? Follow our detailed step-by-step tutorial: Getting started with OLAP cubes. But first, check out the demo!