Some time back I read Chris Webb published content to a blog "Considerations On The Power BI Announcements At The MS Data Insights Summit", where between the lines was this fairly intriguing point:
Throughout the previous few years my clients have asked me when MS planned to deliver SSAS in the cloud and I've generally answered that Power BI is SSAS in the cloud – it's simply firmly combined with a front-end at this moment.
As I'm as of now wanting to move the whole BI engineering of one of my clients to the cloud, this made me think: would we be able to jettison SSAS as far as we might be concerned effectively for Power BI by learning MSBI Online Course
To consider that, I've assembled a few charts to show the possibilities of moving BI to the cloud. To start with, I'll examine the potential designs, at that point the unimaginable engineering (however perhaps the circumstance I was searching for).
One thing I should make reference to prior to making a plunge is the VERY significant contribution on Twitter from @jrolandjones and @SQLChick, just as a somewhat helpful blogpost from Melissa Coates/SQL Chick about Direct Connect.
Update (15:07): It appears I've missed a urgent news update, being Power BI presently having RLS (as of March 31). Here and there it's difficult to keep up. I've refreshed the post appropriately.
Potential Architectures
1: Power BI cloud on existing foundation
The main choice is the current circumstance at my customer's. Added an Enterprise Gateway to a fairly 'exemplary' BI framework, giving Power BI in the cloud. Varieties are unending here - here's the substance I think:
2. All on-prem with the exception of PBI
SSAS has a few uses here:
Semantic Model:
Computation (measures ascertain accurately on various pecking order levels)
Deliberation (stowing away of specialized segments, giving points of view, and so forth)
Column level security (RLS; being not in SQL Server < 2016)
Reserving (execution accomplished through neighborhood/in-mem stockpiling; Columnstore Indexes may limit the exhibition hole however)
2: Move DWH to the cloud
SQL Datawarehouse is Microsoft's cloud offering for a versatile DWH:
Can deal with Petabyte+ information
Upscale/downscale in no time
partition among capacity and register
Power BI can interface straightforwardly to SQL Datawarehouse. Sounds like the engineering can be rearranged, with all BI stuff in any event working on PaaS, which implies less operational stuff to think often about:
3. BI in the cloud - Power BI going about as SSAS
BI in the cloud - Power BI going about as SSAS
In any case, there are a few disadvantages to this choice (given the current contribution of SQL Datawarehouse):
Power BI has restricted capacity capability (10 GB altogether, 250 MB for each model)
SQL Datawarehouse doesn't offer RLS
I haven't tried this, so don't trust me, yet I will in general think SQL Datawarehouse possibly isn't the ideal fit for intelligent (BI purposed) querying1.
3: Using independent SSAS in a cloud framework
To give line level security just as guarantee responsive intuitive inquiries, you could once again introduce SSAS - either on-premises (with an entryway) or in an Azure VM:
4a. SSAS on-prem as semantic and security layer
SSAS on-prem as semantic and security layer
SSAS in VM as semantic and security layer
SSAS in VM as semantic and security layer
In the two cases, SSAS is utilized for everything it was utilized for in the main design:
semantic model
line level security
reserving
Power BI interfaces utilizing Direct Connect, so you will not have the option to wreck and add any demonstrating in there.
Albeit each need tended to by the first framework can be tended to with this new foundation, note that:
The most elevated as-a-administration level for SSAS gave is IaaS
You're not actually restricted in the size of your DWH, as SQLDWH can undoubtedly store in excess of a Petabyte2, which implies SSAS-TM (being in-memory) probably won't do the trick when the dataset is truly enormous. Potential arrangements are utilizing Direct Query, or utilizing Multidimensional (SSAS-MD)3.
The Almost-Ideal Architecture
Perusing all of over, one could contend that the most ideal way is ditch SSAS: carry the semantic model to Power BI, handle RLS in SQL Database, utilize Direct Connect4 to get the information depending on the situation from SQL Database, et presto: BI stage in the cloud! Something like this:
5. Trench SSAS
The Impossible Architecture: RLS in SQL Database, Semantic Model in Power BI
A minor departure from this is handle the RLS in Power BI - perhaps this is surprisingly better, as it makes access control more straightforward:
Minor departure from the Impossible Architecture: RLS in Power BI, Storage in SQL Database
Minor departure from the Impossible Architecture: Semantic Model and RLS in Power BI, Storage in SQL Database
Truth be told: while concentrating all possibilities, this appeared to me as the answers for a completely cloud-based foundation. Really awful it doesn't work out:
SQL Database (just as SQL Datawarehouse) doesn't utilize AD (yet). So while SQL Database offers RLS, Power BI questions are not executed from the client's record . RLS as far as we might be concerned from SSAS can't be cultivated. Joey D'Antoni adjusted me on this one - SQL Database does really uphold Azure Active Directory combined with neighborhood AD, yet I've not had the option to test this one yet (my test climate doesn't have united AD). Stefan Kirner remarks underneath that "The validation mode aad with secret word on sql purplish blue isn't accessible in power bi"
The Direct Connect usefulness from inside Power BI cripples the possibilities to change the semantic model: not any more right computation of measures on all levels of a chain of command
... what remains isn't actually what I needed when I said 'ditch SSAS', for one of the essential motivations to utilize SSAS (the semantic model) is currently away:
5. Modified to the real world
Synopsis: Ready for take-off?
To wrap up, here are the four models talked about moving BI in the cloud. Obviously, there are unlimited possibilities (counting a wide range of strange developments utilizing VMs) yet for the wellbeing of lucidity I've focused in on these four: three potential, one inconceivable (however one that appeared to be a legitimate answer for me when contemplating the whole engineering)
Summed up, I reason that:
There still is no genuine "full cloud" (approximately characterized as "whole BI in any event PaaS") arrangement completely on Power BI + Azure as of now
The agonizingly missing piece for a "full cloud" arrangement is the combination of RLS and a semantic model, best summed up as "SSAS"
Permitting the combination of a semantic model (perhaps inside Power BI) with Direct Connect + expanding the SQL Database with AD-backing would cover the requirements.
Then again: SSAS (counting stockpiling) would eliminate the requirement for a Data Mart in SQL Database
You can move to the cloud however much as could reasonably be expected. Here's a little choice table for potential designs as per the requirements
| Analytic Model > 250 MB (compressed) |
Semantic model needed | Row-level security required |
Ways to go to the cloud |
| No | No | No | #2, but substitute SQLDWH with SQL Database |
| Yes | No | No | #2, use Direct Connect |
| No | Yes | No | #2, build Data Model in PBI and use SQL Database instead of SQL Datawarehouse |
| No | No | Yes | #2, build Data Model in PBI or use SQL Database instead of SQL Datawarehouse |
| Yes | Yes | No | #3, you need SSAS for a semantic model > 250 MB |
| Yes | No | Yes | #4 |
| No | Yes | Yes | #3, you still need SSAS for RLS. Near future: Power BI too |
| Yes | Yes | Yes | #3 for sure |
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