I'm having a real hard time understanding what the difference is between a tabular vs multidimensional model.
Don't both use dimensions and fact tables?
Can't both have a star or snowflake schema?
Don't both have measures and calculated columns?
What is the difference?
Also, if I'm using Power BI and I connect to SQL Server instead of SSAS, I can still do my thing with it. Why is SSAS needed for tabular models if you can just do it in SQL Server?
Don't both use dimensions and fact tables?
Nope. Multidimensional uses Attribute Hierarchies and Measure Groups. Tabular uses Tables, and has no built-in notion of what a "fact" or "dimension" is.
Can't both have a star or snowflake schema?
Yes. And Tabular can have other designs as well. Tabular models can have single-table, or more normalized schemas, although using a star or snowflake design is generally considered a best-practice.
Don't both have measures and calculated columns?
MD does not have calculated columns. See Comparing tabular and multidimensional solutions
Also, if I'm using Power BI and I connect to SQL Server instead of SSAS, I can still do my thing with it.
Nope. Power BI always uses a Tabular or Multi-Dimensional model. When you connect to SQL Server with Power BI you are creating a Tabular model, and either Importing the data into memory, or creating a DirectQuery model (or a hybrid). In either case there is still a Tabular Model created, either embedded in the .PBIX or in a SSAS/AAS server.
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I am creating a dataset In Power BI Desktop using data in a SQL Server database. I have read the sqlbi article that recommends using database views but I would like to know: how do I structure them?
There is already a view of the database that contains most of the information I need. This view consists of 7 or 8 other, more basic views (mainly 2 column tables with keys and values), combined using left joins. Do I import the larger view as a flat table, or each of the smaller views and create a relationships etc, ideally in a star schema, in Power BI?
I guess conceptually I am asking: where does the SQL stop and Power BI start when it comes to creating and importing views?
where does the SQL stop and Power BI start when it comes to creating and importing views?
Great question. No simple answer. Typically modeling in Power BI is quicker and easier than modeling in the database. But modeling in the database enables DirectQuery, and is more useful outside of Power BI.
Sometimes it boils down to who is creating the model. The "data warehouse" team will tend to create models in the database first, either with views or tables. Analysts and end-users tend to create models directly in Power BI.
Sometimes it boils down to whether the model is intended to be used in multiple reports or not.
There is no one-size-fits-all approach here.
If your larger view already has what you need and you need it for just one-off report then you can modify it to add additional fields(data points) considering the trade off for effort needed to create a schema.
The decision weather you should import smaller views and connect them as Star schema ( considering that they have a fact table surrounded by dimension tables) depends on if you are going to use that in lot of other reports where the data is connected i.e. giving you same level of information in every report.
Creating views also depends on lot of other factors, are you querying a reporting snapshot(or read-replicas) of your prod database or you are querying the actual production database. This might restrict you or impact the choice for Views and Materialized Views.
I hope you are doing well.
I'm working on a migration from an on premise ssas multidimensionnal cube to an azure analysis services tabular model.
Is there a way , a method or a tool to do it quickly and efficiently?
It's a large cube and it will take time to develop it from scratch with tabular model.
Thank you for your help
SSAS Multi Dimensional (MD) and Tabular are fundamentally different technologies, there is no quick method of converting one to the other, you will have to rebuild the model from scratch, and the measures etc.
Be aware that some of the things MD models are good at, like calculating up and down hierarchies, Tabular really struggles with. If the cube is fundamentally sound and has good performance, and you want to move it into the cloud service, use a VM in Azure, with SQL Server on it, it may work out cheaper that Azure AS, per month.
I was wondering if anyone here knows the exact differences for these 2 modes, more specifically:
What can we do in one model that we can't do with the other? (Multi-dimensional vs Tabular and vice versa)
How is the data stored in one model versus another?
If I am wring an SSRS / PowerBI / Excel report against this, what limitations does one model have over the other?
Does the tabular model have cubes? If not, what is the alternative storage medium and how does it differ from cubes (maybe provide for me
some background on what cubes are to begin with)
What are the differences in security considerations? As I understand, with the Multi-dimensional model, row-level, column, level
and even cell-level security can be applied - what is available with
this for the tabular model?
Also, as I understand SQL Server 2016 is moving to using the Tabular Model by default and that there may be some differences/improvements
over what is current in use (SQL Server 2014) - can you please provide
a list of what those are?
Thank you so much in advance.
A good place to start might be these articles which should be accurate as to the differences in SSAS 2014.
Advice on the decision points for choosing to build a Tabular or Multidimensional model
Paul Turley’s high-level description of Tabular strengths and weaknesses
Dimension relationships
Summary level presentation
Many-to-many relationships and writeback and scope statements and non-visual dimension security are some of the biggest missing features in SSAS 2014 Tabular in my opinion.
Tabular security is row based and just supports visual totals, not non-visual totals or cell security. But in many cases you don't want to use cell security for performance reasons.
Tabular uses in-memory columnar storage. Multidimensional uses disk-based row-based storage. So scanning a billion row fact table requires reading all columns from disk in Multidimensional and takes a minute or two to return a query on a fact table that large. If you optimize the Multidimensional model by building an aggregation then the query may take seconds. Tabular just scans the columns used in the query and simple queries or calculations even on a billion row table may return in under a second.
With SSAS 2016 Tabular the bidirectional relationship was added which was a very big deal for modeling flexibility and allowing many-to-many relationships. And parallel partition processing made loading large models feasible.
SQL 2017 installer for SSAS has Tabular as the default.
If you have the option for using SSAS 2016 Tabular or above it is highly recommended for performance and modeling flexibility. Here is what's new in SSAS 2016 and SSAS 2017.
I have read all those article about datawarehouse and olap.....however I have some question on it
I have created a datawarehouse using mysql and I also created an API which contain ad-hoc query to query from the datawarehouse, so is this API consider as ROLAP?
Is it possible to create own OLAP? If yes, how?
Usually data warehouse has normalized structure and DWH is not the same as ROLAP.
ROLAP it is technique used to modeling data. ROLAP is usually used for reporting. ROLAP is very good to make analytical query and you can use many reporting (BI) tools to easily build reports on you data.
It isn't necessary to write you own application to build reports. ROLAP (relational OLAP) it is when you model you data as "star" or "snowflake" using facts and dimension tables in traditional RDBMS. It star schemas also called "multidimensional cubes".
By OLAP often is meant MOLAP (multidimensional OLAP) - it's when you really store your data in multidimensional data structure in special data stores (not in RDBMS).
You shouldent create you own MOLAP e data storag- you should use alredy developed OLAP servers like MANDARIN, Pentaho Olap,Essbase, ORACLE EE database with OLAP option.
The confusion you are pointing out comes from the fact that peoples tend to use this term anywhere and in a wrong context.
OLAP applications are precisely defined by the OLAP council. These are applications that fullfill a bunch of requirements. You can read these requirements Here.
In big words, these are analytical oriented applications that allow you to build reports in an a multidimensional fashion (it means you have dimensions and indicators that you can cross) and get fast anwsers at enterprise-scale, with drill down and drill accross capabilities. Something close to OLAP applications is this : http://try.meteorite.bi/
Building an adhoc reporting engine on top of a datawarehouse doesn't mean you have an OLAP application. Does it have a multidimensional shape ? Is it user oriented ? Is it fast enough ? It has to answer yes to all these questions and the ones below to be a candidate to be an OLAP application.
I'm a few months into developing a reporting solution. Currently I am loading a relational data warehouse (Fact and Dimension tables) using SSIS. SSAS cubes and dimensions are then created from the relational Data warehouse. I then use SSRS to build reports using MDX queries.
The problem I have is that things are starting to get rather complicated trying to understand how multidimensional modelling works as well as MDX and cubes. Since the organization it's being designed for is rather small, I'm thinking that I should re-evaluate my approach.
I think maybe I should just eliminate SSAS from the picture and simply create reports that report directly off the relational data warehouse using SQL queries. The relational data warehouse could still be loaded nightly to allow up to date data for reporting.
I'm just wondering if that would be a good idea considering I'm not very experienced with data warehousing and SSAS. Also I wanted to know if keeping my relational data warehouse in dimension and fact tables would still work with SQL queries or would I need to redesign the tables. I don't want to make the decision to eliminate SSAS if that will end up causing more headaches or issues.
The reports will not include complicated calculations besides row counts and YTD percentages. For example "How many callers were male?" and "How many callers called for Product A?" Which are then broken down by month.
Any comments or suggestion are much appreciated cause I'm starting to feel rather frustrated with trying get SSAS cubes developed properly.
I was in a similar situation at my company. I had never used SSAS, and I was asked to do research on the benefits of using cubes to do some reporting. It was a pretty steep learning curve because my background is in development not data and reporting. SSAS is most useful when aggregate queries on a relational database are time consuming and if reports need to be broken down into hierarchies that an analyst can use to better understand the state of the business. Since SSAS stores aggregate info, queries of that nature are very quick. If your organization's data is small, the relational queries might be quick enough that you don't really need the benefit of storing aggregates.
Also you need to take into consideration the maintainability of using SSAS. If you're having trouble figuring out SSAS and MDX then how easy of a time will others? I tried to explain an MDX query I wrote to my boss who is experienced with SQL, but it's really quite different from relational queries. How easy is it going to be to add more complex reports?
A benefit to using SSAS is it can put the analyst in control of the report. Second, there are great tools and support. Finally, it's pretty easy to deploy and connect.
You can remove SSAS from your architecture yes because all the results you can get from an MDX query to SSAS, you can get from a T-SQL query to your datawarehouse because the cube was built reading data from the DW. BUT, bear in mind the following: the main advantage on an OLAP cube, in my point of view, are aggregations.
Very simple explanation: lets say you have a fact table called orders with 1 million orders per month. If you want to know how much you sold on that month, using sql you need to read row by row and sum the value to produce the total. That's like 1 million reads on your DB. If you have a cube, with the propper agrregations configured, you can have that value pre-calculated and pre-stored on your cube so if you need to know how much you sold on a month, you will have only one read to your cube.
Its a matter of analyse your situation, if you have a small cube, maybe aggregations are not necessary and you cna do fine with SQL, but depending on the situation, they can be very helpfull