The Azure Analysis Services is Microsoft’s latest addition to their cloud-based data platforms. It has its roots in the Proven Analytics Engine, i.e. The SQL Server Analysis Services. The Azure Analysis Services is an OLAP Engine of enterprise grade and a BI Modelling Platform and is offered simply as a fully-managed platform in the form of a service (platform-as-a-service, or PaaS).
It lets Developers and the BI Professionals make BI Semantic Models that can fuel highly interactive and also rich Analytical Experiences in BI Tools (like Power BI and Excel) along with custom applications.
|Benefits of Azure Analysis Services||Drawbacks of Azure Analysis Services|
|The Azure cloud offers very high availability||Azure Analysis Services is in constant need of vigilant management|
|Your data is secure with Azure Analysis Services||Azure Analysis Services requires serious platform experience or expertise|
|Azure Analysis Services makes Scaling up or down very easy|
|Azure Analysis Services is very Cost Effective|
|Responses to the user are given at the “Speed of Thought”|
|Developers can now create servers in a matter of seconds|
|Servers can be paused and resumed with no extra charge|
|Different facets of the SQL Servers are fully available|
|Azure Analysis Services is supported by a multitude of Microsoft Tools|
|A huge amount of Memory is available|
Advantages of Azure Analysis Services
- High Availability :
High availability is on offer from Microsoft Azure Cloud along with redundancy in the data centers, on a worldwide scale. As a result, an agreement, on the service level, or SLA, can be offered by Azure of 99.95%. It boils down to around 4.38 hours downtime in a whole year. Most competitors just cannot compete with that.
- Data Security :
Microsoft Azure is strongly focused on security and follows the security model considered standard – Detect then Assess, then Diagnose, followed by Stabilisation and finally, Close. Azure is paired up with cyber security controls that are very strong. This helped it to win several compliance certifications, establishing it as the frontrunner in IaaS Security. Azure also protects the end user.
- Scalability :
Microsoft Azure puts Scalability within the reach of our fingertips. You can use it to scale the computing power, up or down, according to your requirements, with the simple click of the mouse. This scalable structure of Microsoft Azure, makes it incredibly alluring to businesses as it provides them with the flexibility to only pay for things they use.
- Cost Effective :
Now it is always imperative to make sure that an IT budget is not overshot. Azure has a “pay-as-you-use”, pricing that aids SMBs to manage their IT budgets in a better manner. Moreover, the cloud environment is responsible for businesses being able to launch customer applications along with Internal Apps, on the Cloud, simultaneously. Infrastructure, hardware and maintenance cost.
- Speed :
Using the Azure Analysis Services, any BI professional can make up semantic models using raw data and then share it with any business user. They would only need connection to the model and they instantly be able to explore that data. Azure Analysis uses one highly-optimised engine(in-memory) to give immediate responses to the user queries – “speed of thought”.
- Creating Servers :
Within seconds, developers can make a server, selecting from the Developer (D1) or from the Standard(S1, S2, S4) service tiers. Every tier has a fixed capacity when it comes to query processing units and model cache. Developer Tier (D1) can support up to a 3GB model cache while the largest Standard Tier(S4) can support up to 100GB.
- Pausing Servers :
Pausing a server and/or resuming it, costs money. But not with the Azure Analysis Services. Administrators can now pause servers and resume them at any point in time they want to. There will be no charges incurred on pausing the server. Microsoft also plans to allow administrators to scale up or down one server, in the Standard tiers.
- SQL Server :
The Data Tools of the SQL Server are available to the developers in the Visual Studio, so that they have the means to create models and deploy all of them to the service. These models can be managed by the Administrators, by making use of the SQL Server Management Studio. Using the SQL Server Profiler, Administrators can investigate issues.
- Microsoft Tools :
Azure Analysis is supported by an array of Microsoft Tools. They are, Power BI, Microsoft Excel and the SQL Server Reporting Services. The Business Users have the ability to consume models in any of the major BI Tools. Moreover, the other BI Tools, compliant with MDX, can be used too, after the download and installation of the latest drivers.
- Memory :
Memory available for the service tier is both databases that are online along with temporary structures needed to process the database(s), which is comparable to a server’s RAM. The memory size should be enough to store compressed data along with any memory structure of the Analysis Services, and that includes query cache and the processing buffers.
Disadvantages of Azure Analysis Services
- Requires Management :
Azure does not let the end-user consume any information at all. It, on the other hand, moves the business’ computing power to the cloud from the data center or office. Like most of the other providers of cloud services, Azure is also in constant and dire need of expert management and maintenance, that includes patching along with server monitoring.
- Requires Platform Expertise :
Azure, unlike any local server, needs expertise so that the efficient working together, of all the moving parts, is ensured. Business Administrators are often not completely engaged in the operations of their own cloud services. Common mistakes on the computing power of the servers on premises, can translate to thousands of dollars in losses, for the businesses, each year.
As we can see, the Microsoft Azure Analysis Services do have a couple of very important drawbacks. However, it has a host of different features that give these Analysis Services the edge over all of the competition. As you switch to the cloud, making use of the Azure Analysis Services, a few of these features/settings should be focused on, as they might be important for big data models.
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